Eliminating AI Bias: A Human + Machine Approach
By: SARAH HOFFMAN | AUG 27, 2020
Bias in AI is a known problem. Cases involving medical care, parole, recruiting, and loans have all been tainted by flawed data sampling or training data that includes biased human decisions.1 The good news: large organizations are waking up. Even the Vatican has chimed in with a charter on AI ethics.2 Even better news: there are practical methods for combatting AI bias.
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Combat AI Bias with Smart Human Interventions

Emphasize inclusive design. During World War II, the U.S. Air Force faced a high death rate due to cockpit designs based on the body dimensions of the average man, which didn’t match even a single pilot.3 Designing systems with inclusive design in mind — so that it can be accessed and used by as many people as possible — is also needed with AI. Instead of focusing on the “average man,” we need to make sure we account for all our users. Unfortunately, diversity in AI is even more scarce than diversity in technology overall.4 One way to reduce bias in AI is to have more diversity among the workers creating the technology — people who are able to understand, recognize, and care about the potential applications of a system.5

Set up ethics boards and hire AI ethics professionals. Search LinkedIn and you will find jobs with titles like “Senior Director, Trusted Artificial Intelligence,” “AI Ethics and Governance Lead,” and “Director, ML Ethics Transparency and Accountability,” jobs that exist to ensure that AI is being used responsibility. Microsoft’s AI Ethics and Effects in Engineering and Research (Aether) group includes recommendations on regulating the use of facial recognition technology that have prompted the cancellation of significant sales efforts due to concerns about ethical misuse of products.6 Instagram’s newly formed “equity and inclusion team” will examine how Black, Hispanic, and other minority users in the U.S. are affected by the company’s algorithms, including its machine learning systems.7

Upgrade engineer training. Today, many computer science programs include at least one ethics course. Harvard even embeds ethics throughout their computer science curriculum by working with their philosophy department, helping students see how ethical issues can arise from many contexts.8 This type of education shouldn’t stop when one graduates. Engineers should learn about the challenges of measuring accuracy for different demographics, best practices for labeling training data, and how to use inclusive design during beta testing.

Combat AI Bias with Technology

Take advantage of tools that help with AI explainability and fairness. Google, Microsoft, and IBM have developed automated tools to detect and fix bias in AI algorithms.9 Google’s ML-fairness-gym, which was published in open source in early February, lets researchers study the long-term effects of AI’s decisions by simulating outcomes so the fairness of a policy can be assessed.10 “Counterfactual fairness,” a technique that DeepMind is exploring, ensures that a model’s decisions are the same in a counterfactual world where attributes such as race, gender, or sexual orientation, were changed.11 There are also open-source technologies such as Local Interpretable Model-Agnostic Explanations (LIME) that can look for unintended discrimination before it gets into models.12

Combat Human Bias with Smart AI Interventions

Clean up data sets to improve human decision making. When there are known sensitive categories – such as race, gender, marital status, and sexual orientation — AI can be used to make sure that such data can’t be used to nudge humans not to rely too quickly on common cognitive biases. San Francisco scans police reports and automatically redacts race information using AI.13 Companies like Hulu and Twilio use AI tools for recruiting to bring in more diverse candidates who might be overlooked with traditional recruiting methods.14

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