With the increasing uptake of Artificial Intelligence (AI), more attention is given to its potential unintended negative consequences. This has led to a proliferation of voluntary ethical guidelines or AI Principles through which organizations publicly declare that they want to use AI in a fair, transparent, safe, robust, human-centric, etc. way, avoiding any negative consequences or harm.

Harvard University has analyzed the AI Principles of the first 36 organizations in the world that published such guidelines and found 8+1 most used categories[1], including human values, professional responsibility, human control, fairness & non-discrimination, transparency & explainability, safety & security, accountability, privacy + human rights.

The figure below shows the timeline of publication date of the AI Guidelines for the 36 organizations. The non-profit organization Algorithm Watch maintains an open inventory of AI Guidelines with currently over 160 organizations[2].

From Principles to practice

While there is much work dedicated to formulating and analyzing AI Guidelines or Principles, much less is known about the process of turning those principles into organizational practices (Business as Usual – BAU). Initial experiences are being shared and published[3]^,[4], and experience is building up[5]^,[6],[7], with technology and consultancy companies leading. Telefonica’s methodology, coined “Responsible AI by Design”³, includes various ingredients:

• The AI principles setting the values and boundaries[8]
• A set of questions and recommendations, ensuring that all AI principles are duly considered in the product lifecycle
• Tools that help answering some of the questions, and help mitigating any problems identified
• Training, both technical and non-technical
• A governance model assigning responsibilities and accountabilities

Here we focus on a new organizational role which is essential for implementing the responsible use of AI in an organization; the role plays a critical role in the governance model and we have coined it “Responsible AI Champion”[9] (RAI Champion).

Introducing the Responsible AI Champion

Why do we need champions? AI & Ethics is a new area in many organizations and to establish new areas the identification of champions is a proven strategy. A champion is knowledgeable about the area, is available for fellow employees in a given geography or business unit, and provides awareness, advice, assistance and escalation if needed. Champions are also crucial to turn new practices into BAU, and as such are agents of chance. In particular, the responsibilities of a Responsible AI Champion are to inform, educate, advice & escalate, coordinate, connect and manage change.

Inform

A RAI Champion informs fellow employees about the importance of applying ethics to AI and data to avoid unintended harm. He or she raises awareness of the organization’s AI Principles.

Educate

A RAI Champion provides and organizes training -both online and face to face – to the corresponding business unit or geography, explaining how to apply the principles to the product lifecycle. He or she also explains the governance model and encourages self-educated experts to form a voluntary community of practice where “local” employees can get first-hand advice.

Advice & escalate

A RAI Champion is the final “local” contact for ethical questions about AI and Big Data applications. If the experts of the community of practice, nor the RAI Champion can address the issue at hand, it is escalated to a multi-disciplinary group of senior experts.

Coordinate

Given the fact that AI and Big Data refer to issues dealt with in several other organizations, RAI Champions need to coordinate with all of them. Coordination is needed with the DPO (data protection officer) for privacy related issues; with the CISO (chief information and security officer) for security-related aspects; with the CDO (chief data officer) for data and AI related topics; with CSR (corporate social responsibility) for reputational and sustainability issues; with the Regulation area for possible future AI regulations; and with the Legal area for other legal issues.

In some organizations, the responsible use of AI and Big Data is part of a wider “responsibility” initiative including topics such as sustainability (SDGs), climate change, human rights, fair supply chain and reputation. In this case, the RAI Champion should coordinate and be fully aligned with the respective leaders.

Connect

RAI Champions need to connect relevant people to form communities of experts on the subject matter. Those communities are the first place to go if ethical doubts cannot be solved within a product or project team. RAI Champions also need to form a community among themselves connecting different geographies and business units of the organization in an active learning and sharing network. Finally, more mature organizations also may consider setting up or join an external RAI Champion (or similar) network where experiences and practices are shared with other organizations, either from the same sector or across different sectors.

Manage change

Finally, RAI Champions are agents of change. They have to ensure that over time, ethical considerations become an integral and natural part of any business activity touching AI and Big Data, including design, development, procurement and sales. They have to implement and turn the governance model into BAU.

 

The RAI Champion profile

For organizations that are starting, the RAI Champion is more a role than a fulltime job. Typically, the role is taken up by AI or Big Data enthusiasts that have researched ethics topics by themselves without being asked and are attentive to the latest developments. But the RAI Champion role is not necessarily the realm of technical people only. They also come from areas such as regulation, CSR, and data protection. Indeed, a “good” candidate to take up the role is the DPO.

RAI Champions need to be communicative with an interest to teach and convince. As with any new roles with an interdisciplinary character, RAI Champions will need to be trained before they can exercise their role.

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• [1] https://cyber.harvard.edu/publication/2020/principled-ai
• [2] https://arxiv.org/abs/2001.09758
• [3] https://arxiv.org/abs/1909.12838v2
• [4] https://inventory.algorithmwatch.org/
• [5] https://www.weforum.org/agenda/2020/01/tech-companies-ethics-responsible-ai-microsoft/
• [6] https://cltc.berkeley.edu/wp-content/uploads/2020/05/Decision_Points_AI_Governance.pdf
• [7] https://www.ai.mil/blog_04_01_20-shifting_from_principles_to_practice.html
• [8] https://www.telefonica.com/en/web/responsible-business/our-commitments/ai-principles
• [9] Independently, Telefonica and Microsoft have come up with the same name: RAI Champion.