Ethical Leadership and Governance

Behaving responsibly isn’t always easy—particularly as the world continues to grapple with the ethical implications of advanced new AI solutions—but it almost always pays off in the long run.

How should companies begin to think about ethical data management? What measures can they put in place to ensure that they are using consumer, patient, HR, facilities, and other forms of data appropriately across the value chain—from collection to analytics to insights?

Eliminating algorithmic bias can unlock value for your company.

A closer look at the role AI plays in health care delivery is warranted to appreciate its current strengths, limitations, and ethical complexities.

The Artificial Intelligence (A.I.) Brain Chip will be the dawn of a new era in human civilization. The Brain Chip will be the end of human civilization.

Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved?

More than half of consumers participating in a recent survey say they would purchase more from a company whose AI interactions are deemed ethical.

Tech companies are realizing that artificial intelligence changes power dynamics and as providers of A.I. and machine learning systems, they should proactively consider the ethical impacts of their inventions.

AI is not a panacea for all our problems. In fact, it can be quite dangerous in the wrong hands.

The General Data Protection Regulation (GDPR) creates a seemingly large hurdle for artificial intelligence (AI) implementation. Yet it can also be an opportunity for ensuring your AI is maximising the privacy of individuals.

From disease to terrorism, using AI for social good - rather than simply business - is becoming an increasingly popular idea.

Only a few legislators really know what they’re talking about, but it’s a start.

It’s not easy to encourage the ethical use of artificial intelligence. But here are 10 recommendations.

A panel of experts weighs in on whether responsible AI should be connected to corporate social responsibility efforts.

Historically, it’s been a matter of trial and error. There’s a better way.

In NewVantage Partners 2019 Executive Survey, more than half of executives — 55.7% — pointed to data ethics as a top business priority.

There’s more talk of AI ethics than ever before. But talk is just that—it’s not enough.

As AI is deployed to sensitive areas like military, judicial and surveillance use, governments and companies have responded that so long as their algorithms are accurate, they are ethical, suggesting there is considerable confusion about the difference between AI correctness and AI ethics.

If we don’t know how AIs make decisions, how can we trust what they decide?

Tim O’Brien explains his role as Microsoft’s first full-time manager for AI policy and ethics.

The rush toward ethical AI is leaving many of us behind.

Applications that use machine learning (ML) can make predictions that help inform better decisions. Here’s our six key principles for responsibly integrating ML.

As the clamor for so-called “ethical AI” nears its crescendo, consultants recommend steps for creating fair and balanced algorithms. Hint: There’s no magic bullet.

A new report says current initiatives to fix the field’s diversity crisis are too narrow and shallow to be effective.

It’s not the algorithm behaving badly, but how we define fairness that determines an artificial intelligence system’s impact.