The customer experience continues to evolve. As consumers become empowered due to digitization, and their interactions with brands become more trackable and data-driven, companies must understand their customers more completely and intimately than ever before to differentiate themselves from their competition. This level of in-depth understanding of the consumer comes from robust, clean, and accurate customer data — both observed and predicted. Today, a data-driven customer experience is a better CX.

To achieve this type of dynamic customer experience organizations are investing in advanced tools like customer data platforms (CDPs), enhanced with [predictive AI software](/), enabling companies to gather and combine data into unified profiles so they can understand their customers more cohesively. Companies use those AI-driven insights to tailor a superior CX which can help improve the quality of customer interactions and resulting behaviors, like reducing churn. The predictive AI / predictive analytics software market is so integral to the development of a dynamic, data-driven CX that it is estimated to grow to $14.52 billion by 2028, according to [Statistica](https://www.statista.com/statistics/1286871/predictive-analytics-market-size/).

Customer acquisition costs (CAC) continue to grow, and in challenging economic times, organizations everywhere are looking to contain costs while maximizing value from their most valuable customers. When one considers that the cost of acquiring a new customer can be as much as five times as to retain one, improving retention strategy should be top of mind for any company that wants to survive and thrive in the modern business world.

By deploying a more AI-driven customer-centric approach to the CX, organizations can meet the rising expectations of today's customers, delivering the right CX at the right time to the right person, according to consulting firm [Capgemini](https://www.capgemini.com/insights/research-library/shaping-the-ai-enabled-customer-experience/).

Companies who deploy an AI-enabled customer experience can deliver a:

- 10% boost in revenue
- 20% increase in customer satisfaction
- 60% reduced effort in audience selection
- 13% decrease in operational costs

Unlike more traditional approaches, AI enables companies to continually tune their understanding of customers and dynamically reshape the CX to provide them, and the business, with true value. All to build long-term value (LTV) with your best customers.

## The challenges of observed data

What type of data is shaping today's modern data-driven CX? A few years ago, that would have been mostly from data mined from traditional survey-based methodology, what is today called zero-party data.

Zero-party data, according to Forrester, is 'data that a customer intentionally and proactively shares with a brand.' In other words, it is solicited data. It's also observed data, which is data that was collected by observing and recording events and behaviors as they occur without manipulation. While zero-party is very desirable data, due to its accuracy and direct relationship with the consumer, it's not the bread and butter of today's CX. That would be reserved for observable first-party data.

The customer data shaping today's CX is primarily made up of unsolicited first-party data like pages visited, clicks, adds to cart, time of day, and time spent browsing. This is the real data making up the data-driven CX. However, this data is reactive, and in the past. It needs to be enhanced with more predictive capabilities to deliver the dynamic data needed to meet a modern consumer's customer experience expectations.

The solution to providing the type of forward-looking data needed to create great customer experiences is for companies to begin leveraging predictive AI capabilities, like predictive modeling and predictive analytics derived via machine learning (ML), to enable companies to anticipate customer behaviors and see CX challenges and exploit opportunities in real-time.

## Predictive data drives the customer journey

Organizations that are powered by predictive insights can understand their customers as full people, interpret their interactions more completely, and trigger them at the right moments in their path along the customer journey for a desired action.

The use of predictive analytics and predictive modeling establishes a fundamental sea-change in how a dynamic customer experience is delivered to consumers. The reality is predictive customer behaviors will increasingly drive the unified customer experience until all customer interactions are predictively driven. Is your organization prepared to leverage AI-powered consumer insights for both business and customer value?

Leveraging consumer data for value is THE key differentiator for a successful modern business. But having quality, accurate data is only part of the challenge. The rubber meets the road when you determine how you will deploy that data to improve business performance, customer value, and ROI.

But all data is not equal. To do more forward-looking activities with your customer data, like train machine learning algorithms with it to inform AI, you will need to enrich it with third-party data and deploy the right software that enables predictive AI capabilities.

## How Faraday can get you there

[Faraday](/) provides the data and the predictive AI modeling infrastructure (IaaS) that enables organizations to iterate on their business challenges, instead of their technology infrastructure. With our platform, brands, and the software companies who support them, can now develop end-to-end customer experiences, at scale, driven by predictive insights. Predicting customer behavior enables more effective and efficient customer engagement within the CX. In business terms, this translates to better conversion rates which in turn creates higher revenue and reduces cost.

In the end, a data-driven CX is a better CX. A quality predictively driven CX will mean better retention for your most valuable customers, helping to reduce new customer acquisition costs in favor of a more long-term value retention strategy.

Faraday is the only predictive AI platform, both for [software teams](/solutions/powered-by-faraday) and [brands](/features/customer-behavior-predictions), dedicated to predicting customer behavior. Everything you need to make customer experiences predictive is built into our software.

## Looking forward

Predictive AI and Predictive Analytics deliver to companies the tools they need to increase customer retention and improve the customer experience. By leveraging customer data and predictive modeling, brands can understand their customer's behavior better than ever before and calculate churn for each customer.

Customer experiences everywhere are becoming more and more predictive, driven by advanced AI. It's only a matter of time until all customer experiences are predictive.

For more information on how to make your customer experience predictively driven by AI, contact [Faraday](/talk-to-sales).

As customer experiences evolve to be more data-driven, companies must leverage predictive AI and customer data platforms to anticipate customer needs, enhance interactions, and improve retention, thereby reducing costs and boosting long-term value.

Predictive customer experience is the future

_This customer story has been anonymized to respect the company’s competitive strategy._

A leading CRM used by roofers leveraged Faraday’s infrastructure to power their Lead Intelligence tool, which helps their customers see which leads are likely to convert so that no time is wasted on bad fits.

## Giving clients an edge

The product team at this CRM for roofers is focused on helping users find value in their product, and in the five years before adopting [customer predictions](/features/predictions), they observed a 10% drop in their user base’s conversion rate. After investigating why, the product team found that, as the customer's lead generation tools grew more advanced and varied, the lead data quality was reduced, and it became more difficult for their users to see what was valuable to them. With this in mind, they determined that they needed better ways for users to cut through the noise and surface their most valuable leads–and they settled on a solution in AI [lead scoring](/solutions/lead_management).

> “We wanted to be able to help our customers’ sales teams close more deals faster. Faraday’s lead scoring and data could help us do just that.” - **VP of Product at a leading CRM for roofers**

## Finding a solution and time-to-market that fits

Although the product team unearthed an excellent opportunity to help their customers, building AI lead scoring entirely in-house was out of the question, as they didn’t have the resources to do so, and they knew that time was of the essence, so the long timeline wasn’t going to work for them. They also knew they needed purpose-built infrastructure to scale up as they grew.

With Faraday, this roofing software company found an infrastructure designed to predict lead and customer behavior and comes with a [consumer identity graph](/features/data) that includes data on nearly every U.S. adult. This–plus Faraday’s 10+ years of experience–gave their product team the confidence to build and launch a successful product in their desired timeline.

> “If we’d had the resources to build this pipeline ourselves fully, we were looking at a time-to-market of over a year. We integrated with Faraday in about three months, and it’s been worth it for that alone–not to mention the data and expertise we’ve received.” **VP of Product at a leading CRM for roofers**

## Making predictions actionable

The roofing CRM’s software engineers embedded Faraday’s lead scores into their product and provided a way to filter lead results by lead rank. With lead rank, every lead and prospect in the roofing software is assigned a rank based on how likely they are to convert, which is displayed as the lead rank status. Before partnering with Faraday to surface this likelihood, The roofing CRM’s users might spend days nurturing a sales opportunity only to have it fall flat because there wasn’t a good fit.

Their users can easily filter by the highest lead rank, which perfectly fits the ultimate goal at the start–to surface actionable insights that help focus roofing sales teams.

[![Image of roofing CRM user interface](/images/blog/leading-roofing-crm-lead-scoring/roofing-crm-ui.png)](/images/blog/leading-roofing-crm-lead-scoring/roofing-crm-ui.png)

In the first six months since launch, the roofing CRM’s clients who have turned on the new feature close deals 10% faster than those who haven’t–netting an additional $3k+ per month for your average contractor. The product team at the CRM for roofers is thrilled with the results so far and are keen to leverage more of Faraday’s infrastructure to build additional features, focusing on [explainability](/blog/what-is-explainable-ai-why-is-it-important) to give users a clear sense of why an individual lead is receiving its score.

[Create a free Faraday account](https://faraday.ai/signup) today.

A leading CRM used by roofers powers their Lead Intelligence tool with predictive lead scores, which helps their customers see which leads are most likely to convert.

How a leading CRM used by roofers employs Faraday to help clients close deals 10% faster

_This article is a followup to our [bias reporting](/blog/faraday-bias-reporting) article, and aims to provide a high-level overview of how Faraday users can mitigate harmful bias._

## What is bias mitigation in machine learning?

Bias mitigation in machine learning encompasses a set of methodologies and techniques devised to diminish and ideally eradicate unjust and discriminatory predictions inherent in the decision-making processes of artificial intelligence (AI) systems. This is a key component of [Responsible AI](/features/responsible-ai).

Bias, often unintentional, may infiltrate these systems as a result of the historical data they are shown, leading to inequitable treatment of specific demographic groups. For example, there is a history of [discrimination](https://www.investopedia.com/the-history-of-lending-discrimination-5076948) against specific minority groups in financial lending, and this history can perpetuate bias if it is not intentionally mitigated.

It is important to note that not all algorithmic bias is harmful. Predictions about, for example, the likelihood of buying products that are exclusively relevant to, say, certain age groups will and should discriminate in favor of those age groups. For the remainder of this article, when we discuss bias, we’re excluding innocuous bias like this and focusing on harmful bias.

Bias mitigation ensures that AI systems render decisions characterized by impartiality and fairness, irrespective of variables such as race, gender, or other sensitive attributes. This undertaking is pivotal in the development of AI systems that uphold principles of fairness and equality for all individuals.

The subsequent sections of this article describe how Faraday’s bias mitigation feature allows users to create fair propensity predictions. For more on bias management in Faraday, read our [bias management announcement](/blog/faraday-bias-management-announcement).

## How to reduce harmful bias in machine learning

When predicting propensity, Faraday can mitigate bias in two broad ways:

1. The first step is to ensure that the predictive algorithm considers every demographic group as equal. In order to do so, Faraday rebalances your historical data before submitting it to the algorithm for pattern discovery.
2. Second, when later making predictions, Faraday will adjust the nominal propensity ranking to ensure that the highest scorers for each demographic are ranked highly overall. This ensures that the predictions are meeting the fairness objective while maximizing predictive lift.

Some Faraday terminology will be used here, and will link to documentation.

### Unbiasing historical data

In order to mitigate bias in a client’s existing customer data, Faraday assigns different “weights” to each customer in the data to fine-tune their influence on the model's learning process. The goal is to address and rectify biases present in the data, ensuring that the model gives equal consideration to all relevant examples.

To understand this process, consider an example: a home mortgage company. Given historical disparities arising from discriminatory laws and government policies (among other reasons), some demographics are less likely to appear in the company’s customer data.

Formally, Faraday will discover this by noticing a difference between the “attainment” (all customers) and “eligible” (regional adult population) [cohorts](/docs/product/cohorts) in one or more dimensions of concern. For example, we could find fewer women than men in the historical customer data, even though the regional population is an even split: a disparity that has no rational basis. Without correction, the algorithm would believe (incorrectly) that there is some intrinsic quality which makes men more likely mortgage customers than women.

To unbias the historical data, Faraday would give higher weights to customers in underrepresented or disadvantaged groups, increasing their influence on the algorithm while it is looking for salient patterns. Building on the mortgage example above, Faraday could give higher weight to the proportionally fewer women customers, evening out their influence versus men. This results in more equitable predictions for individuals belonging to these groups.

By adjusting the weights assigned to different customers, Faraday aims to create a more balanced and fair learning process, ultimately leading to AI models that provide fairer outcomes across diverse demographic groups.

### Unbiasing predictions

When you include a propensity [outcome](/docs/product/outcomes) in your [pipeline](/docs/product/pipelines), Faraday can take additional measures to rectify potential biases in the resulting predictions. After the initial predictions are made, this approach evaluates and adjusts them to ensure fairness and reduce discriminatory outcomes.

Given that the first step eliminates bias in the historical customer data, why is this second step even necessary? When we unbias historical data, we can't alter the algorithm to completely ignore the overrepresented population in favor of the underrepresented one–we’re simply telling it to pay more attention to the latter until it has a roughly equal representation. This weighting can have a cascading effect that creates biases of its own, though.

To take the mortgage example even further, maybe the women customers tend to be older than the non-customers, whereas the men customers tend to be younger than their non-customer counterparts–after the weight adjustment, this can make the trait _age_ mean that an individual is more likely to attain the outcome if they are older, unfairly penalizing those without that characteristic, which is why this step is crucial.

In this step, Faraday looks for differences between the demographic makeup of the pipeline’s eligible population and the subgroup receiving the highest scores. The predictions are then adjusted to align more closely with the desired fairness objectives.

This method allows for the correction of biases that may have been present in the original predictions, promoting a more fair distribution of outcomes across different demographic groups. By incorporating fairness considerations after the initial predictions are generated, prediction this re-ranking contributes to developing machine learning models that exhibit improved fairness and reduced problematic disparity in their decision-making processes.

## Mitigating harmful bias with Faraday

As of the publication of this article (February 2024), Faraday currently allows for bias detection and mitigation for the **age** and **gender** dimensions (these features are often referred to as protected classes or Dimensions Of Concern).

When you define a propensity outcome in Faraday, you can choose how you want the system to handle bias:

- **No mitigation**: The outcome fairness is only reported as part of the outcome dashboard. No further transformation is applied to ensure that protected classes are treated fairly. This is perfectly appropriate in cases where the product or service being sold is intrinsically linked to certain demographic groups.
- **Equality**: This mitigation strategy ensures that demographic patterns are similar between the eligible population and the target group (those with the highest scores). For example if “young adult, women” represent 25% of your eligible population, we ensure that any population targeted from the pipeline will contain 25% of “young adult, women”.

  The chart below depicts the effects of historical data bias mitigation using the “equality” setting with three populations (A, B, C). Population **A is underrepresented** in the customer data (attainment population)—30% are customers vs. 47% in the overall population while **B** and **C are both overrepresented**. The mitigation process ensures that in the target group, population **A** accounts for 47% while **B** and **C** account for their original 14% and 39% respectively.

  [![Equality bias mitigation](/images/blog/faraday_bias_mitigation/equality-mitigation.png)](/images/blog/faraday_bias_mitigation/equality-mitigation.png)

- **Equity**: This mitigation strategy “inverts” bias so that populations that were historically underrepresented are now instead prioritized.

  In the example below, the customer group (attainment cohort) consists of 30% of population **A**, 20% of population **B**, and 50% of population **C**. The equity mitigation is going to promote the underrepresented populations **A** and **B** at the expense of population **C**. The target population will contain 37% of **A**, 47% of **B**, and 16% of **C**.

  [![Equity bias mitigation](/images/blog/faraday_bias_mitigation/equity-mitigation.png)](/images/blog/faraday_bias_mitigation/equity-mitigation.png)

Note that when mitigating bias, Faraday will consider all dimensions simultaneously, leading the system to consider subpopulations like “young adult, women” and “middle-aged men.”

## Does mitigating bias hurt performance?

Because the bias mitigation process necessarily alters the ranking and promotes sub-populations with otherwise lower predicted scores, it can reduce lift.

That said, the impact is often small. The top two graphs below depict the impact of **equality** mitigation on Gender. The lift curve for the un-mitigated model is only slightly better than the one for the mitigated model. The main difference lies in the 20% to 40% score percentile range with lift in that range decreasing from ~300% to ~220%.

The two bottom graphs show the mitigation at work. On the left, the un-mitigated model will create a target group with 2x too many males.

After mitigation, the graph on the right shows only a slight discrepancy between the fair proportion of each gender, within 3%. This is a significant fairness improvement over the initial target population breakdown with only a minor reduction in performance.

[![Bias mitigation comparison](/images/blog/faraday_bias_mitigation/mitigation-comparison.png)](/images/blog/faraday_bias_mitigation/mitigation-comparison.png)

## Why mitigate bias?

Bias isn’t inherently harmful. Earlier in this article, we touched on innocuous bias that occurs when products or services are explicitly designed for and marketed toward certain groups, like a womens’ swimwear brand marketing mostly to women. It’s the _other_, harmful–and often hidden–bias that should be surfaced and mitigated.

With Faraday, bias mitigation is easy. If you find that a predictive outcome you created has bias that you want to mitigate, simply create a new outcome using the same cohorts of people, toggle mitigation on for the sensitive dimension in question, select your mitigation strategy, and save the outcome. Once it finishes building, compare the bias report from the un-mitigated outcome with the report from the mitigated outcome. Alternatively, use the **bias_mitigation** object with the API call to [create](/developers/reference/createoutcome) an outcome.

[![Bias mitigation comparison](/images/blog/faraday_bias_mitigation/bias-mitigation-in-app.png)](/images/blog/faraday_bias_mitigation/bias-mitigation-in-app.png)

It’s in the best interest of anyone developing or using AI to do so responsibly. Whether you’re using [generative AI](https://www.bloomberg.com/graphics/2023-generative-ai-bias/) for email copy and creative, predicting customer behavior like we walked through in this article, or something else entirely, you should be wary of the existence of bias in the data that the algorithms are using, and how it impacts who you market and sell to. By employing responsible, [explainable AI](/blog/what-is-explainable-ai-why-is-it-important), you can build a foundation of trust between not only yourself and your customers, but between your business and your data.

Responsible AI isn't just good business–it’s the right thing to do.

Mitigating harmful bias with Faraday is simple, whether through our developer API or in-app. Learn more about how Faraday mitigates bias in your customer predictions.

How Faraday helps mitigate harmful bias in machine learning

Regardless of what part of your business you find yourself in, your team has likely discussed implementing artificial intelligence (AI) of some sort in 2024–and it’s no wonder, with [33% of businesses](https://thesocialshepherd.com/blog/ai-statistics) already using AI and another 42% considering adopting it, the benefits of harnessing AI are too good to be ignored.

As leaders, employees, and consumers alike learn more about predictive models, machine learning algorithms, and various other pieces of the AI puzzle, questions naturally arise about how AI really works. Can the predictive models created be trusted? Can they produce biased results? Making complex machine learning algorithms and their results explainable to humans has proven to be a challenging feat for organizations to overcome in a tangible way, though there are significant benefits for those that do.

Put simply, if you’re going to change the way you interact with customers based on what AI tells you, how can you trust what it’s telling you?

## What is explainable AI?

Historically, AI has been plagued by the [black box problem](https://umdearborn.edu/news/ais-mysterious-black-box-problem-explained), in which artificial intelligence reaches purportedly valuable conclusions—finding high-scoring leads, producing compelling generative content, and so on—and the user is left with the option to take it or leave it. No insight was provided into how the AI reached the conclusion it did, and what data it used to do so, thus the term “black box.”

Explainable AI aims to solve this problem by providing much-needed insight into the inner workings of predictive models so that the end-result of a predictive model offers both the sought-after conclusions, as well as how those conclusions were reached. This means that deploying [customer predictions](https://faraday.ai/features/predictions) and other AI-generated content can be done with confidence, rather than a hand-wave.

## Why does explainable AI matter?

[According to IBM](https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/business-trends-2024), 9 in 10 consumers say trust is the most important deciding factor when deciding which brand to adopt. People have never been more keenly aware of the importance of brand trust, and how quickly it can vanish. The need for consumers to trust brands they interact with has resulted in the need for explainable AI, and for the black box to be removed. IBM also reports that general consumer trust in businesses dropped from 48% in 2021 to 20% in 2023, meaning that businesses need to employ frameworks like explainability that help establish and maintain trust in the relationship for consumers to even consider making a purchase. The relationship between trust and explainability extends far beyond direct-to-consumer goods, too.

In [finance](https://faraday.ai/blog/how-to-optimize-ads-for-compliance-in-finance)—a space that has historically struggled with bias—AI can be used to determine whether or not an applicant should receive a loan, which leaves the door open for their machine learning algorithms to use historically biased data to permit or deny a loan. AI must be explainable in order for lenders, or anyone else building or employing it, to consider it for use.

## Benefits of explainable AI

Explainable AI enables businesses that employ AI pipelines to properly interpret and understand its output without requiring data science degrees. This benefit is twofold: first, the importance of understanding the tools you’re working with rather than just taking their word for it can’t be overstated. Knowledge is power, after all. This leads straight into the second, in that it allows you to not only understand why the AI reached the conclusion it did, but then enables you to take that reasoning a step further by analyzing its context. If you’re using machine learning algorithms to find out which of your customers is [most likely to churn](https://faraday.ai/predict/category/churn_scores), you can dig into its explainability to reveal what traits about them helped make that critical decision. Similarly, explainable AI can help [reveal and mitigate bias](https://faraday.ai/blog/faraday-bias-management-announcement) in your data, which helps you stay compliant with local laws and regulations.

Additionally, a foundation of trust is built (and maintained) between the business and its customers, which is a rare commodity in 2024. With existing customers generating [72% of company revenue](https://www.hubspot.com/hubfs/HubSpots%202024%20Sales%20Trends%20Report.pdf), a happy customer base that you’ve nurtured with trust will keep coming back for more–and they’ll refer their friends, too.

## Challenges of explainable AI

With such impactful benefits on the table, why hasn’t explainable AI become mainstream? The simple answer is that it’s difficult. Entire classes of ML algorithms are fundamentally incapable of effectively explaining their inferences, so you have to choose your methodology carefully. Explainability also requires well-prepared and structured attributes that not every organization is lucky enough to have on the shelf. Finally, in a commercial environment where data science resources are both scarce and overburdened, AI explainability never quite seems to make it to the top of the todo list.

## Explainability is built into Faraday

While businesses that are fully committed to building their own AI pipelines need to overcome the hurdles involved in implementing explainable AI, many businesses are instead adopting platforms like Faraday that are purpose-built for using AI–and in Faraday’s specific case, predicting customer behavior.

Faraday has long been a proponent of [responsible](https://faraday.ai/features/responsible-ai), explainable AI. In fact, it’s one of our founding principles. Every [predictive model](https://faraday.ai/blog/faraday-probability-scores) that Faraday users create allows customization of what traits are used, allows removal of protected class data, and comes with a full report that details things like the most important traits that helped build the model. In 2023, we released [score explainability](https://faraday.ai/blog/faraday-score-explainability), a feature that outlines each of the most influential pieces of data within an individual person’s predictions, and how much influence they had.

[![Image of a Faraday score explainability output](/images/blog/explainable_ai/explainability-output.png)](/images/blog/explainable_ai/explainability-output.png)

Let’s use the above graphic to visualize the churn prediction example in the previous section. Jane Madison has a 75% probability of churning, and the explanation provided in the predictive model that scored Jane said that the fact that Jane is a millennial played a major role in why she’s so likely to churn. Meanwhile, John’s children made it less likely that he would churn. Not only do we know that Jane is more likely to churn than John, but we know _why_.

In the end, explainable AI results in tangible benefits for both the businesses employing it and their customers. Businesses are able to understand their own tools, and use that understanding to reveal impactful pieces of insight like _why exactly_ Jane Madison is going to churn. From a consumer perspective, businesses employing explainable AI stand out as trustworthy and worth interacting with–and worth referring their friends to do the same.

With AI being built into many businesses' workflows, concerns are rightfully being raised about its trustworthiness, and emphasis put on the importance of explainable AI.

Why explainable AI is so important for businesses

With increased expectations to provide meaningful customer experiences—and shrinking budgets—brands are looking for smart engagement techniques that use the tools they already know, love, and pay for. Join our team of experts from Faraday, Google, and Hightouch as we delve into the world of predictive customer engagement, offering insights on how you can elevate your strategy with your existing stack.

**In this session you’ll:**

- Uncover the potential of predictive models like propensity, spend forecasting, next-offer recommendations, and personas in enhancing and personalizing your customer engagement strategy.
- Gain valuable insights into the mechanics behind crafting precise predictions.
- Learn practical strategies for seamlessly deploying insights into your customer engagement tools.

With increased expectations to provide meaningful customer experiences—and shrinking budgets—brands are looking for smart engagement techniques that use the tools they already know, love, and pay for.

How to unlock data-driven customer engagement with predictive AI

Quantifying bias in predictive models is the first step towards building models with fair treatment of protected groups.

Faraday bias reporting: How we measure & report on bias

One of Faraday’s founding pillars is the use of [Responsible AI](/features/responsible-ai), which includes both preventing direct harmful bias and reporting on possible indirect bias. We’ve been building these features directly into the platform over the past several months, and today we’re going to walk through two new features in Faraday with **bias reporting** and **bias mitigation**.

## Bias reporting

To see the bias report on an outcome, head to [outcomes](https://app.faraday.ai/outcomes) and scroll down to the **bias** section to see the breakdown of bias discovered in the _data_, _power_, _predictions_, and _fairness_ categories, which we'll dive into below. These can also be retrieved via the API using [get outcome analysis](/developers/reference/getoutcomeanalysis).

[![Bias reporting on an outcome](/images/blog/faraday_bias_reporting/outcomes-bias-reporting.png)](/images/blog/faraday_bias_reporting/outcomes-bias-reporting.png)

Faraday uses four categories to report on bias for an outcome:

- **Data**: Measures selection bias in the underlying cohorts used in the outcome. In a Faraday outcome, two labels exist for the purpose of this blog post: _positive_, or the people from the attainment cohort that were previously also in the eligibility cohort, and _candidate_, or the people from the eligibility cohort.
- **Power**: Measures outcome performance on a subpopulation compared to baseline performance–for example, Faraday will compare how well the outcome performs on the subpopulation “Senior, Male” compared to everyone else.
- **Predictions**: Measures proportions of subpopulations being targeted compared to baseline proportions in order to see if the subpopulation is over or under-represented.
- **Fairness**: Measures overall fairness using a variety of common fairness metrics in the literature. For example, Faraday will look at whether or not the subpopulation "Senior Male" is privileged or underprivileged.

Each bias category uses groups of people called subpopulations, which are combinations of people defined in the [understanding bias reporting](/docs/product/outcomes#understanding-bias-reporting) section's **sensitive dimensions** dropdown table. Examples of these are "Teens with gender unknown" and "Senior Females." We're launching with two of these sensitive dimensions in **age** and **gender**, but will be rolling out more over time.

If you discover that one of your outcomes includes harmful bias, you can create a new outcome using the same cohorts, but enable bias mitigation options while creating it to compare results.

## Bias mitigation

When creating a new outcome, you can choose to mitigate bias by using the toggles in the **bias** section. Check out our [outcomes documentation](/docs/product/outcomes#mitigating-bias) for info on different bias mitigation strategies.

[![Bias mitigation in an outcome](/images/blog/faraday_bias_reporting/outcomes-bias-mitigation.png)](/images/blog/faraday_bias_reporting/outcomes-bias-mitigation.png)

<Callout type="warning" title="Taking action on bias">
Mitigating bias isn't something you should set-and-forget, meaning it's not recommended that you toggle both **age** and **gender** on for every single outcome you create. For example, if you're a womens' swimwear brand, your data will skew heavily toward women, and mitigating gender would negatively impact your lift.
</Callout>

Always keep the populations that you're making predictions on in mind when considering bias. In the above example, it's _totally_ fine to ignore the biased skew towards a certain gender or age group because they're your ideal customers.

We're thrilled to continue to drive Responsible AI forward by providing agency for our users to both discover and mitigate bias in their customer predictions. Stay tuned for a deep dive into the science behind how Faraday determines bias.

Learn how Faraday's bias management suite enables you to analyze and mitigate bias found in your customer predictions.

Introducing Faraday bias management

I've always loved Zapier. It really does feel like a superpower to set up a workflow with a zap: the gift that keeps on giving. This section has been on their homepage longer than anything else and I can see why — Zapier users all feel this way.

[![Testimonials from Zapier users](/images/blog/zapier/testimonials.png)](/images/blog/zapier/testimonials.png)

That's why I'm so excited and proud to share that you can now [incorporate Faraday predictions into your zaps](https://zapier.com/apps/faraday/integrations). With this new integration, you can score leads, assign customers to personas, make product recommendations to shoppers, forecast spend, and more, all within your automated Zapier workflows.

Predictions are the key to improving customer engagement throughout the lifecycle—you can personalize outreach, customize offers, prioritize better opportunities—the list goes on.

## Working through an example Zap with Faraday predictions

Let's say we want to score new leads when they arrive in HubSpot, evaluating them for their likelihood to close and assigning them to a persona. This way, you'll be able to tailor your handling of these leads differently according to their predictions.

### Faraday setup

We'll assume you already have basic customer and lead data, along with their respective cohorts, set up in your Faraday account. If you're new to Faraday, check out the [docs](/docs/intro/welcome) and [Faraday Academy](/docs/academy) to get up to speed.

Next, you'll need to set up your two predictions if you don't have them already:

1. **Lead scoring outcome** — [recipe](https://app.faraday.ai/recipes/lead_scores_in_lookup_api) or [docs](/docs/how-to/predictions/likelihood-to-convert)
2. **Customer personas** — [recipe](https://app.faraday.ai/recipes/persona_assignments_in_lookup_api) or [docs](/docs/how-to/predictions/customer-personas)

Finally, if you don't already have it, create a pipeline to get these predictions deployed:

- **Population** – everybody
- **Payload** – the two predictions above
- **Deployment** – Lookup API

That last bullet is important to get things working right on Zapier.

### Creating your zap with Zapier

I love Zapier's new LLM interface for creating zaps, and it works great here. Just head to your dashboard and type "When a new lead comes into HubSpot, score it using Faraday," like so:

[![Zapier prompt saying "When a new lead comes into HubSpot, score it using Faraday"](/images/blog/zapier/prompt.png)](/images/blog/zapier/prompt.png)

Zapier should automatically calculate your zap template for you—something like this:

[![Zapier template for "Score new leads in HubSpot with Faraday"](/images/blog/zapier/template.png)](/images/blog/zapier/template.png)

Hit that "Try it" button. This should load the template up in the zap editor, which will look something like this:

[![Zapier zap editor for "Score new leads in HubSpot with Faraday"](/images/blog/zapier/editor.png)](/images/blog/zapier/editor.png)

Click the HubSpot box to pull up settings for this trigger step. You'll have to choose your HubSpot account and then pick your lead capture form from the dropdown. It will look like this:

[![Zapier HubSpot settings for "Score new leads in HubSpot with Faraday"](/images/blog/zapier/hubspot.png)](/images/blog/zapier/hubspot.png)

Hit the test button, and it will find a recent lead from your form data. When you hit continue, you'll make your way into the Faraday action step settings.

Choose or add your Faraday account (you'll need your Faraday API key for this), then proceed to the Action section. In the "Select pipeline" dropdown, choose the pipeline you created earlier. If it's not on the list, make sure your Lookup API deployment was properly added to this pipeline in Faraday.

Then, use the form data from HubSpot to fill out as many of the identity fields as you can. In this case, we capture email in our lead form, so I'm using that (actually, the Zapier LLM had that preconfigured for me ✨):

[![Zapier Faraday settings for "Score new leads in HubSpot with Faraday"](/images/blog/zapier/faraday.png)](/images/blog/zapier/faraday.png)

After testing what you've got so far, it's time to store your predictions for this new lead somewhere. You could put them back in HubSpot with an "Update Contact" action, or whatever you'd like. Then publish your zap and you're all set!

**Ready to try Faraday with Zapier? [Create a free account](https://faraday.ai/signup) to get started today.**

Zapier is everybody's favorite workflow automation tool. Now, you can retreive predictions from Faraday in your Zaps.

Launch: Faraday predictions now in Zapier

Faraday is thrilled to highlight **LeadConduit**, the ActiveProspect integration for Faraday. Between LeadConduit helping you acquire, optimize, and distribute leads, and Faraday scoring your leads with likely-to-convert predictions, your leads have never been easier to manage. Read on for details on how ActiveProspect and LeadConduit can help scale your lead management efforts:

[ActiveProspect](https://activeprospect.com/) is a comprehensive SaaS platform that empowers companies across industries to scale quality lead acquisition from any source, protect against litigation risk by documenting proof of consent, and accelerate speed-to-lead with automated process flows. With almost 20 years of experience in the industry, ActiveProspect has developed a reputation as the industry-leading consent-based marketing platform.

ActiveProspect’s three main products –[TrustedForm](https://activeprospect.com/trustedform/), [LeadConduit](https://activeprospect.com/leadconduit/), and [LeadsBridge](https://leadsbridge.com/)– are designed to help businesses capitalize on marketing opportunities and maximize conversions.

- [TrustedForm](https://activeprospect.com/trustedform/#howitworks) is the de facto standard for documenting express written consent by providing independent certificates of authenticity used for legal compliance and campaign optimization.
- [LeadConduit](https://activeprospect.com/leadconduit/#howitworks) streamlines the leading buying process to help businesses efficiently acquire customers at scale. Custom lead flows automatically enhance and filter leads in real time to deliver the highest-quality prospects to your CRM or lead buyer.
- [LeadsBridge](https://leadsbridge.com/platform/) automates your customer acquisition workflows from major ad platforms with seamless integration to your revenue tech stack. Utilize over 380 integrations to sync your leads, target custom audiences, and conversion tracking to boost sales and productivity.

Overall, [ActiveProspect](https://activeprospect.com/get-started/) offers the only complete solution for consent-based marketing powered by patented technology. Its TrustedForm, LeadConduit, and LeadsBridge products are just a few examples of the company's commitment to delivering high-quality solutions that help businesses succeed in today's increasingly competitive marketplace.

**Ready to try Faraday? [Create a free account](https://faraday.ai/signup).**

Faraday is thrilled to highlight LeadConduit, the ActiveProspect integration for Faraday. Between LeadConduit helping you acquire, optimize, and distribute leads, and Faraday scoring your leads with likely-to-convert predictions, your leads have never been easier to manage.

Faraday integration spotlight: LeadConduit

AI is on everyone’s minds due to its myriad use cases and applications across industries. From chatbots getting people where they want to be faster, to providing [financial planning advice](https://quickbooks.intuit.com/au/blog/accountants-and-bookkeepers/accounting-ai/), to using ChatGPT to help out with a resume or piece of code, to making [customer predictions](/features/predictions) for likelihood to take an action and personalization–AI isn’t going anywhere anytime soon.

[64% of businesses](https://www.forbes.com/advisor/business/ai-statistics) feel that various aspects of their organization will see increased productivity due to AI, and it’s no surprise when you think about the countless tasks that people have been doing for ages–segmentation, data analysis, and so on–and how much time & effort is saved by offloading them to AI. The use of AI for generating content in particular has scaled so fast that discussions abound on the impact that this AI revolution can and will have on human jobs, and brands like [NPR](https://www.npr.org/2023/06/07/1180768459/how-to-identify-ai-generated-deepfake-images) and [MIT](https://www.technologyreview.com/2022/12/19/1065596/how-to-spot-ai-generated-text/) are publishing guides for how to spot AI-generated content.

## The black box problem

The issue at the heart of this AI quandary is known as the [black box problem](https://umdearborn.edu/news/ais-mysterious-black-box-problem-explained). In short, the problem is that while what we’re seeing AI accomplish is incredible–and seemingly accurate in many ways, at first glance–by and large, we don’t understand how it reaches its particular conclusions, and what data it uses to reach them.

Faraday has long pursued making AI that uses [responsible data](/features/responsible-ai) to make responsible predictions accessible for all, and part of this pursuit has been tackling the black box problem. Starting today, every customer propensity prediction made using Faraday infrastructure can include _explainability metadata_.

## Explainable AI now in Faraday

[![Image of explainability checkbox in Faraday](/images/blog/product/explainability/explainability-checkbox.png)](/images/blog/product/explainability/explainability-checkbox.png)

Sounds good, but what does this mean?

Faraday has always provided detailed technical reports that analyze the impact of key data points on predictions at the overall level.

Now, each prediction, down to the individual level, has its score explained by the features in your data that had the highest impact on that individual’s prediction.

[![Image of a Faraday outcome feature importance for number of children and age](/images/blog/product/explainability/explainability-features.png)](/images/blog/product/explainability/explainability-features.png)

In the above example outcome, we see that, overall, the **approximate number of children** and **age** are the most predictive factors. And we can see what ranges lead to what average impact. For example, people with no kids have—all else equal—a slightly higher probability than those with 1–2 kids. This kind of aggregate reporting has always been available in Faraday.

[![Image of a Faraday score explainability output](/images/blog/product/explainability/explainability-output.png)](/images/blog/product/explainability/explainability-output.png)

Above, we see an example of CSV output from a pipeline. John is impacted by this outcome’s major factors, age and number of children, and leaves him with a low probability.

Jane, on the other hand, exhibits a more unusual combination of traits that give her a much higher probability for different reasons. For Jane, both her household income and millennial lifestyle saw her conversion probability higher than John because this business often sees conversions from people with those traits–even if they’re not the dominant traits.

## Explainable AI is Responsible AI

The central idea of Responsible AI is to understand our models—how they work, why they produce the results they do—enough to identify and correct their shortcomings. That’s why we have invested so heavily in, for example, bias detection and mitigation here at Faraday.

But even as we develop a strong understanding of and confidence in our models in the aggregate, we must not lose sight of the impact that prediction can have on individuals. We should demand nothing less from our AI models than that they explain themselves. Explainability is as core to Responsible AI as transparency and fairness.

A simple statement that explains a given prediction may not seem like much at first glance, but we feel otherwise. The _just trust us_ hand-wave that has been the motto engraved on the black box around AI for decades has been preventing our industry from moving forward with respect and responsibility for businesses and people alike–until now. Now you can see exactly what data helped AI reach the conclusion that it did, and feel confident that you’re making responsible predictions with responsible data.

**[Create a free Faraday account](https://faraday.ai/signup) today.**

Learn how every individual you make customer predictions on had their score calculated.

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