Ethics and AI: 3 Conversations Companies Need to Have - Deepstash

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Ethics and AI: 3 Conversations Companies Need to Have

Ethics and AI: 3 Conversations Companies Need to Have


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The Premise

While concerns about AI and ethical violations have become common in companies, turning these anxieties into actionable conversations can be tough. With the complexities of machine learning, ethics, and of their points of intersection, there are no quick fixes, and conversations around these issu...

AI Ethics

Over the past several years, concerns around AI ethics have gone mainstream. The concerns, and the outcomes everyone wants to avoid, are largely agreed upon and well documented.

No one wants to push out discriminatory or biased AI. No one wants to be the object of a lawsuit or regulatory i...

Understanding The Problems That Need To Be Solved

Acting quickly to address concerns is admirable, but with the complexities of machine learning, ethics, and of their points of intersection, there are no quick fixes. To implement, scale, and maintain effective AI ethical risk mitigation strategies, companies should begin with a deep understandin...

Summon a senior-level working group that is responsible for driving AI ethics in your organization. They should have the right skills, experience, and knowledge such that the conversations are well-informed about the business needs, technical capacities, and operational know-how. Involve four kin...

The Tech Guys

You need the technologist to assess what is technologically feasible, not only at a per product level but also at an organizational level. That is because, in part, various ethical risk mitigation plans require different tech tools and skills. Knowing where your organization is from a technologic...

The Legal Eagles

Legal and compliance experts are there to help ensure that any new risk mitigation plan is compatible and not redundant with existing risk mitigation practices. Legal issues loom particularly large in light of the fact that it’s neither clear how existing laws and regulations bear on new technolo...

The Path Correctors

Ethicists are there to help ensure a systematic and thorough investigation into the ethical and reputational risks you should attend to, not only by virtue of developing and procuring AI, but also those risks that are particular to your industry and/or your organization. Their importance is parti...

The Business Leaders

Business leaders should help ensure that all risk is mitigated in a way that is compatible with business necessities and goals.

Zero risk is an impossibility so long as anyone does anything. But unnecessary risk is a threat to the bottom line, and risk mitigation strategies also should be ...

Three Conversations to Push Things Forward

Once the team is in place, here are three crucial conversations to have.

  • One conversation concerns coming to a shared understanding of what goals an AI ethical risk program should be striving for.
  • The second conversation concerns identifying gaps between where the organizatio...

Define Your Organization’s Ethical Standard for AI

Any conversation should recognize that legal compliance (like the anti-discrimination law) and regulatory compliance are table stakes.

The question to address is: Given that the set of ethical risks is not identical to the set of legal/regulatory risks, what do we identify as the ethi...

Identify the Gaps Between Where You Are Now and What Your Standards Call For

There are various technical “solutions” or “fixes” to AI ethics problems. A number of software products from big tech to startups to non-profits help data scientists apply quantitative metrics of fairness to their model outputs.

Tools like LIME and SHAP aid data scientists in explaining ho...

Know The Limits

Your AI ethics team should determine where their respective limits are and how their skills and knowledge can complement each other. This means asking:

  • What, exactly, is the risk we’re trying to mitigate?
  • How does software/quantitative analysis help us mitigate tha...

Understand the Complex Sources of The Problems and Operationalize Solutions

Many conversations around bias in AI start with giving examples and immediately talking about “biased data sets.” Sometimes this will slide into talk about “implicit bias” or “unconscious bias,” which are terms borrowed from psychology that lack a clear and direct application to “biased data sets...

Productive conversations on ethics should go deeper than broad stroke examples descried by specialists and non-specialists alike. Your organization needs the right people at the table so that its standards can be defined and deepened.

Your organization should fruitfully marry its quantitat...



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