Good discussion paper on the Role and Expertise of AI Ethicists
Bottom line – it’s a mess!
In the less than 2 years, AI Ethicists have become common. You see it in social media profiles, speaker profiles, especially in academia. Where did they all come from, what is their role and what is their actual expertise?
Few studies have looked at what skills and knowledge these professionals need. This article aims to fill that gap by discussing the specific moral expertise of AI Ethicists, comparing them to Health Care Ethics Consultants (HC Ethicists) in clinical settings. As the paper shows, this isn’t very clear, leading to vastly different interpretations of the role.
It’s a mess! A ton of varied positions that lack consensus on professional identity and roles, a lack of experience in the relevant areas of expertise, especially technical, lack of experience in real-world applications and projects and a lack of established practical norms, standards and best practices.
As people who have as their primary role the bridging the gap between ethical frameworks and real-world AI applications, relevant expertise, experience, skills and objectivity are required. The danger is that they remain too theoretical and can be bottlenecks if they do not have the background to deliver objective and practical advice. There is a real problem of shallow and missing expertise along with the ability to deliver practical outcomes and credibility.
Problem with the paper
The paper focus on job roles, as advertised, but misses the mass of self-proclaimed, internally appointed and simply identified as doing the role without much in the way of competence-based selection. Another feature of the debate is the common appearance of ‘activists’ within the field, with very strong political views. They are often expressing their own political beefs, as opposed to paying attention to the law and reasonable stances on ethics – I call this moralising, not ethics.
However, it’s a start. To understand what AI Ethicists do, they looked at LinkedIn profiles to see how many people in Europe identify as AI Ethicists. They also reviewed job postings to figure out the main responsibilities and skills needed, using the expertise of HC Ethicists as a reference to propose a framework for AI Ethicists. Core tasks for AI Ethicists were also identified.
Ten key knowledge areas
Ten key knowledge areas were outlined, such as moral reasoning, understanding AI systems, knowing legal regulations, and teaching.
K-1 Moral reasoning and ethical theory
● Consequentialist and non-consequentialist approaches (e.g., utilitarian, deontological approaches, natural law, communitarian, and rights theories).
● Virtue and feminist approaches.
● Principle-based reasoning and case-based approaches.
● Related theories of justice.
● Non-Western theories (Ubuntu, Buddhism, etc.).
K-2 Common issues and concepts from AI Ethics
● Familiarity with applied ethics (such as business ethics, ecology, medical ethics and so on).
● Familiarity with ethical frameworks, guidelines, and principles in AI, such as beneficence, non-maleficence, autonomy, justice and explicability (Floridi & Cowls, 2019).
K-3 Companies and business’s structure and organisation
● Wide understanding of the internal structure, processes, systems, and dynamics of companies and businesses operating in the private and public sectors.
K-4 Local organisation (the one advised by the AI Ethicist)
● Terms of reference.
● Structure, including departmental, organisational, governance and committee structure.
● Decision-making processes or framework.
● Range of services.
● AI Ethics’ resources include how the AI Ethics work is financed and the working relationship between the AI Ethics service and other departments, particularly legal counsel, risk management, and development.
● Knowledge of how to locate specific types of information.
K-5 AI Systems
● Wide understanding of AI+ML technology’s current state and future directions: Theory of ML (such as causality and ethical algorithms) OR of mathematics on social dynamics, behavioural economics, and game theory
● Good understanding of other advanced digital technologies such as IoT, DLT, and Immersive.
● Understanding of Language Models – e.g., LLMs – and multi-modal models.
● Understanding of global markets and the impact of AI worldwide. Employer’s policies
● Technical awareness of AI/ML technologies (such as the ability to read code rather than write it).
● Familiarity with statistical measures of fairness and their relationship with sociotechnical concerns.
K-6 Employer’s policies
● Informed consent.
K-7 Beliefs and perspectives of the stakeholders
● Understanding of societal and cultural contexts and values.
● Familiarity with stakeholders’ needs, values, and priorities.
● Familiarity with stakeholders’ important beliefs and perspectives.
● Resource persons for understanding and interpreting cultural communities.
K-8 Relevant codes of ethics, professional conduct, and best practices
● Existing codes of ethics and policies from relevant professional organisations (e.g. game developers, software developers, and so on), if any.
● Employer’s code of professional conduct (if available).
● Industry best practices in data management, privacy, and security.
K-9 Relevant AI and Data Laws
● Data protection laws such as GDPR, The Data Protection Act and so on.
● Privacy standards.
● Relevant domestic and global regulation and policy developments such as ISO 31000 on risk.
● AI standards, regulations, and guidelines from all over the world.
● Policy-making process (e.g., EU laws governance and enforcement).
K-10 Pedagogy
● Familiarity with learning theories.
● Familiarity with various teaching methods.
Five major problems
They rightly argue that AI Ethicists should be recognized as experts who can bridge ethical principles with practical applications in AI development and deployment. Unfortunately this is rare on the ground. It is a confusing field with a lots of thinly qualified, low level commentators self-appointing themselves as ethicists.
- Some, but few in my experience, have any deep understanding of moral reasoning and ethical theories or applied ethics.
- As for business or organisational experience few seem to have been in any real positions relevant to this role within working structures.
- Another often catastrophic failing is the sometimes the lack of awareness of what AI/ML technology is, along with the technical and statistical aspects of fairness and bias.
- A limited knowledge even of GDPR is often apparent and the various international dimensions to the law and regulations.
- As for pedagogy and teaching – mmmm.
Conclusion
To be fair much of this is new but as the paper righty says, we need to stop people simply stating they are ethicists without the necessary qualifications, expertise and experience of the practical side of the role. AI Ethicists are crucial for ensuring the ethical development and use of AI technologies. They need a mix of practical moral expertise, real competences in the technology, a deep knowledge of the laws and regulations, and the ability to educate others to navigate the complex ethical issues in AI. At the moment the cacophony of moralising activists need to give way and let the professionals take the roles. Establishing clear competencies and professional support structures is essential for the growth and recognition of this new profession.
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