RESEARCH REPORT

In brief

In brief

  • Organizations face a difficult challenge when it comes to ethically-informed data collection, sharing and use.
  • There is growing demand for incorporating ethical considerations into products and services involving big data, AI and machine learning.
  • Outside of mere legal compliance, there is little guidance on how to incorporate this ethical consideration.
  • To fill this gap, Northeastern University and Accenture explore the development of effective and well-functioning data and AI ethics committees.


Organizations face a difficult challenge when it comes to ethically-informed data collection, sharing and use. On the one hand, there is increased sensitivity to ethical issues and desire for responsible stewardship of people’s information, both internal and external to organizations. On the other hand, outside of mere legal compliance, there is very little guidance for organizations about what being ethically responsible involves, let alone how to incorporate ethical consideration into product and service design, particularly at scale.

Even among organizations that have adopted institutional value statements for data/AI ethics platforms, there has been limited success translating them to organizational practices, decision-making and products. This situation is problematic for stakeholders from within and outside the organization. For individuals, privacy, data control, security and fairness are at stake. For organizations, there are new risks concerning data misuse and insecurities, with potential for loss of trust from users and consumers.

Not using people’s information illegally is the minimum responsibility for organizations, and it will not be enough to sustain trust, manage risk or be responsive to stakeholder expectations.

Some organizations are exploring the use of ethics committees to help build capacity for data and AI ethics. However, up to now there has been almost no resources from which to draw to guide development of effective and well-functioning data and AI ethics committees.

This report is intended to help fill that gap by:

Discussing

The advantages of a committee-based approach to data and AI ethics.

Describing

The components of a committee based approach to data and AI ethics.

Identifying

The questions that an organization would need to answer in the process of developing an effective ethics committee.

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Why use an ethics committee?

A committee-based model has been used effectively in several contexts, such as protecting human research subjects, supplying guidance in health care contexts, and providing oversight for embryonic stem cell research. It has several features that make it well-suited for building organizational data and AI ethics capacity.

A well-designed committee will:

Bring together

People with the range of expertise needed to effectively analyze, assess and respond to complex problems.

Be responsive

To rapid advances in technological capabilities and to novel applications.

Develop

Standards, cases, precedence and resources to be used in decision-making processes.

Constitute a governance body

That can learn, adapt, and be a repository for institutional knowledge.

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Getting started

Ethics committees can take a wide variety of forms and roles. Crucial to beginning the building process is putting together the right team of people and engaging with organizational stakeholders to begin to think through the key questions about function, values, principles, location, composition and process. Once the basic outlines of the committee are established and the initial committee is formed, quite a lot of the operational details will be developed in the context of the committee’s work.

There is no existing data and AI ethics committee template and the field of data and AI ethics is still maturing. Creating meaningful and effective ethics committee oversight models not only offers benefits and protection to the organization, it is critical to the broader data and AI ethics development process.

Northeastern researchers team up with Accenture to offer a road map for ethics oversight for artificial intelligence.

Ronald Sandler

Professor of Philosophy


John Basl

Associate Professor of Philosophy


Steven C. Tiell

Senior Principal – Responsible Innovation, Accenture

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Get the essentials

The aim of this report has been to help identify the types of questions that need to be answered in the building of an effective ethics committee.

The Big Read

45 minute read

Building data and AI ethics committees

Read more about the advantages of a committee-based approach to data and AI ethics and how to develop an effective ethics oversight committee.

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Executive Summary

Explore highlights of the types of questions that need to be answered in the building of an effective ethics committee.

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