We’ve seen it before, we’re seeing it again now with the recent Apple and Goldman Sachs alleged credit card bias issue, and we’ll very likely continue seeing it well into 2020 and beyond. Bias in AI is there, it’s usually hidden, (until it comes out), and it needs a foundational fix.
This past weekend we saw just how quickly the Apple Card, managed by Goldman Sachs, issue spiraled out of control. What started as a tweet thread with multiple reports of alleged bias (including from Apple’s very own co-founder, Steve Wozniak and his spouse), eventually led to a regulator opening an investigation into Goldman Sachs and their algorithm-prediction practices.
The problem
If we dig into the allegations to find the source of the problem, two issues stand out:
- The algorithm making credit decisions for the Apple card is biased
- The customer support teams from Goldman Sachs and Apple had zero insight into how the algorithm worked when they were asked to explain certain decisions
For (1) above we saw multiple responses to the original tweet thread that corroborated the allegation. Multiple people had faced a similar outcome where all other input factors being the same (or in some cases higher: like a higher annual income or credit score), and gender being the only difference, they were given significantly lower credit limits than their male spouse. This definitely comes across as problematic. Following the allegations, a separate group of non-related women and men ran a test experiment to check for bias and noticed significant differences in credit limits. Men with bad credit scores and irregular income got better offers than women with high incomes and good credit scores.
Was the algorithm biased against women? We can’t say for sure because we don’t know. This is the key - we don’t know what’s going on inside the algorithm to analyze the root-cause. But based on external outcomes, we’re guesstimating that this is likely what happened.
Impact of this problem
The primary issue here is with the black-box algorithm that generated Apple’s credit lending decisions. As laid out in the tweet thread, Apple Card’s customer service reps were rendered powerless to the algorithm’s decision. Not only did they have no insight into why certain decisions were made, they were unable to override it.
Humans don’t want a future ruled by algorithms, especially biased ones. Algorithms permeate all aspects of our lives today from lending and housing decisions to decisions in criminal justice. If we continue to let algorithms operate the way they do today, in the black-box and without human oversight, it results in a dystopian view of the world where unfair decisions are made by unseen algorithms operating in the unknown.
https://twitter.com/dhh/status/1193774524312911874
The solution
How could this issue have been avoided or at least handled better? Let’s come back to the initial statement above on how bias in AI needs a foundational fix. It’s very likely that in this particular credit lending decision, the algorithm was trained on biased data to begin with. To better understand this, let’s look at the high-level lifecycle of an AI solution:
- Identify a use case for AI (credit lending, criminal justice, cancer prediction, etc.)
- Access historical data to build models
- Import this data to train and test models
- Test and validate models
- Deploy models into production
- Monitor models in production to ensure optimal performance
If the data is flawed to begin with, this flaw permeates into everything that an algorithm does going forward. What we need is a way to check for bias and other issues in both data and models through all stages of the AI lifecycle. What we also need is human oversight - AI is just simply not ready to function on its own. We need humans-in-the-loop who will ensure that AI is functioning as it should.
In the Apple credit card example above, it’s likely this issue could have been avoided if humans had visibility into every stage of the AI lifecycle. They could have seen examples in the test and validate stage of how the model was behaving when a certain input factor was isolated and compared with the global dataset. They could have also had the ability to override an algorithm’s prediction in the test/validate stage if they felt it was unfair or incorrect. This would have resulted in an algorithm that was getting trained in the right way to produce accurate results when in production.
https://twitter.com/dhh/status/1193948432466972672
How Fiddler solves for this
This is exactly what we’re addressing at Fiddler. We’re working to unlock the AI black-box and empower all relevant stakeholders with more visibility into their AI than exists today. We’re working on infusing visibility and insight with explainable AI into every stage of an AI solution’s lifecycle: right from the data and training of it to when a model is deployed and in production.
- We dive into the details to explain each prediction a model makes - whether a training, test, or production model - so users can understand the why behind individual decisions.
- Our goal is to empower users with easy to grasp explanations of AI decisions. This empowers different stakeholders in an organization: data scientists are empowered to build the best and most accurate models, risk officers are empowered to publicize models with minimal-risk, and customer support representatives are empowered to answer customer questions around the why behind decisions.
- With Fiddler’s explainable AI built into the AI lifecycle, it helps teams ensure they are compliant with regulations and are protecting their algorithms from inherent and hidden bias.
We’re continuing to build capabilities into Fiddler to ensure explainability is infused throughout the AI lifecycle and are working with a variety of customers to build this functionality into their existing and new models. If you’re interested in working with us, please reach out.
