Ethical Leadership and Governance

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?

Technology has staggering potential to support social needs and is well suited to keep pace with the complex challenges faced by communities, and can do so at scale.

The C-suite has a new denizen. A few forward-looking companies have instituted a chief ethics officer position. This executive helps steer corporate values more broadly—and recently has been at the center of the discussion of how AI algorithms get used.

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

How can companies ensure they use powerful technologies such as AI without having a negative social impact? How can they exploit big data without invading personal privacy? And how can they protect their reputations and the integrity of their products and services when under constant attack by cybercriminals?

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.

The reality is that, for many leaders, there is no true straight-and-narrow path to follow. You beat the path as you go. Therefore, ethical leadership relies a lot on your personal judgment..

Entrepreneurs must start by trying to solve a real problem and build in consumer safety features from the outset.

Blockchain could be a a wild card that disrupts the way we tackle climate change and other environmental challenges.

The hottest acronym in asset management is ESG. Funds that take account of environmental, social and governance factors when deploying capital have already sucked up more than $1 trillion to date. More of them are becoming activist investors, too. Last week alone, Amazon, JPMorgan and BP faced votes at their annual meetings about issues varying from gender diversity to climate risks to the use of artificial intelligence. Chevron and Exxon Mobil are due to face similar scrutiny this week.

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.

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.

For boards, understanding and shaping the forces that drive behavior should move to center stage.

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

Explications par Anne Bouverot, présidente de Technicolor et présidente de la Fondation Abeona et Thierry Delaporte, directeur général délégué du groupe Capgemini, pour l'Institut Montaigne.

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

We don’t allow people to sell their kidneys. We shouldn’t let them sell the details of their lives, either.

A bill introduced by senators Mark Warner and Josh Hawley would require big tech companies to disclose the data they collect and value it for each user.

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

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

Antitrust action alone won’t solve all the problems posed by companies like Facebook, Google, and Amazon.

Asset managers have become a major player in the push for sustainable business, in some cases withdrawing their investments from companies that fail to meet the mark. With trillions of pounds at stake, it doesn’t take a finance expert to see why. So, what do businesses need to do to keep their investors on board?

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

We must keep in mind that just because we have the ability to deploy a new technological innovation does not mean that we should. The need to prioritize digital ethics is becoming increasingly important for all organizations that are mindful about the imprint that they are leaving on society.

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