Useful categorisation of ethical themes

I was at the seminar the other day where I was fortunate enough to encounter Josephine Young from www.methods.co.uk (who mainly do public sector work in the UK).

Josie recently carried out an analysis of the main themes relating to ethics and AI that she found in a variety of sources related to this topic. I have reported these themes below with a few comments. 
Many thanks, Josie for this really useful and interesting work.

THEMES

(Numbers in brackets reflect the number of times this issue was identified).

Data

Data treatment

Data treatment, focus on bias identification (10)
Interrogate the data (9)

Data collection / Use of personal data

Keep data secure (3)
Personal privacy – access, manage and control of personal data (1, 5, 6)
Use data and tools which have the minimum intrusion necessary – privacy (3)
Transparency of data/meta data collection and usage (8)
Self-disclosure and changing the algorithm’s assumptions (10)

Data models

Awareness of bias in data and models (8)
Create robust data science models – quality, representation of demographics (3)
Practice understanding of accuracy – transparency (8)

robotethics.co.uk comment on data: Trying to structure this a little, the themes might be categorised into [1] data ownership and collection (who can collect what data, when and for what purpose), [2] data storage and security (how is the data securely stored and controlled without loss and any un-permitted access [3] data processing (what are the permitted operations on the data and the unbiased / reasonable inferences / models that can be derived from it) and [4] data usage (what applications and processes can use the data or any inferences made from it).

Impact

Safety – verifiable (1)
Anticipate the impacts the might arise – economic, social, environmental etc. (4)
Evaluate impact of algorithms in decision-making and publish the results (2)
Algorithms are rated on a risk scale based on impact on individual (2)
Act using these Responsible Innovation processes to influence the direction and trajectory of research (4)

robotethics.co.uk comment on impact: Impact is about assessing the positive and negative effects of AI in the future, whether that be in the short, medium or long term. There is also the question of who is impacted as it is quite possible that the impact of any particular AI product or service might impact one group of people positively and another negatively. Therefore a framework of effect x timescale x affected persons/group might make a start on providing some structure for assessing impact.

Purpose

Non-subversion – power conferred to AI should respect and improve social and civic processes (1)
Reflect on the purpose, motivations, implications and uncertainties this research may bring (4)
Ensure augmented – not just artificial – AI (8)
Purpose and ecology for the AI system (10)
Human control – choose how and whether to delegate decisions to AI (1)
Backwards compatibility and versioning (8)

robotethics.co.uk comment on purpose: Clearly the intent behind any AI development should be to confer a net benefit on the individual and/or the society generally. The intent should never be to cause harm – even drone warfare is, in principle, justified in terms of conferring a clear net benefit. But this again raises the question of net benefit to whom exactly, how large that benefit is when compared to any downside, and how certain it is that the benefit will materialise (without any unanticipated harmful consequences). It is a matter of how strong and certain the argument is for justifying the intent behind building or deploying a particular AI product or service.

Transparency

Transparency for how AI systems make decisions (7)
Be as open and accountable as possible – provide explanations, recourse, accountability (3)
Failure transparency (1)
Responsibility and accountability for explaining how AI systems work (7)
Awareness and plan for audit train (8)
Publish details describing the data used to train AI, with assumptions and risk assessment – including bias (2)
A list of inputs used by an algorithm to make a decision should be published (2)
Every algorithm should be accompanied with a description of function, objective and intended impact (2)
Every algorithm should have an identical sandbox version for auditing (2)

robotethics.co.uk comment on transparency: Transparency and accountability are closely related but can be separated out. Transparency is about determining how or why (e.g. how or why an AI made a certain decision) whereas accountability is about determining who is responsible. Having transparency may well help in establishing accountability but they are different. The problem for AI is that, by normal human standards, responsibility resides with the autonomous decision-making agent so long as they are regarded as having ‘capacity’ (e.g. they are not a child or insane) and even then, there can be mitigating circumstances (provocation, self-defence etc.). We are a long way from regarding AIs as having ‘capacity’ in the sense of being able to make their own ethical judgements, so in the short to medium term, the accountability must be traceable to a human, or other corporate, agent. The issue of accountability is further complicated in cases where people and AIs are cooperatively engaged in the same task, since there is human involvement in both the design of the AI and its operational use.

Civic rights

A named member of staff is formally responsible for the algorithm’s actions and decisions (2)
Judicial transparency – auditible by humans (1)
3rd parties that run algorithms on behalf of public sector should be subject to same principles as government algorithms (2)
Intelligibility and fairness (6)
Dedicated insurance scheme, to provide compensation if negative impact (2)
Citizens must be informed when their treatment has been decided/informed by an algorithm (2)
Liberty and privacy – use of personal data should not, or not be perceived to curtail personal liberities (1)
Mitigate risks and negative impacts as AI/AS evolve as socio-technical systems (7)

robotethics.co.uk comment on civic rights: It seems clear that an AI should have no more license to contravene a person’s civil liberties or human rights than another person or corporate entity would. Definitions of human rights are not always clear-cut and differ from place to place. In human society this is dealt with by defaulting to local laws and cultural norms. It seems likely that a care robot made in Japan but operating in, say, the UK would have to operate according to the local laws, as would apply to any other person, product or service.

Highest purpose of AI

Shared prosperity – economic prosperity shared broadly to benefit all of humanity (1)
Flourishing alongside AI (6)
Prioritise the maximum benefit to humanity and the natural environment (7)
Shared benefit – technology should benefit and empower as many people as possible (1)
Purpose of AI should be human flourishing (1)
AI should be developed for the common good (6)
Beneficial intelligence (1)
Compatible with human dignity, rights, freedoms and cultural diversity (1, 5)
Align values and goals with human values (1)
AI will prevent harm (5)
Start with clear user need and public benefit (3)
Embody highest ideals of human rights (7)

robotethics.co.uk comment on the higher purpose of AI: This seems to address themes of human flourishing, equality, values and again touches on rights. It focuses mainly on, and details, the potential benefits and how these are distributed. These can be slotted into the frameworks already set out above.

Negative consequences / Crossing the ‘line’

An AI arms race should be permitted (1)
Identify and address cybersecurity risks (8)
Confronting the power to destroy (6)

robotethics.co.uk comment on the negative consequences of AI: The main threats are set out to be in relation to weapons, cyber-security and the existential risks posed by AIs that cease to be controlled by human agency. There are also many more subtle and shorter term risks such as bias in models and decision making addressed elsewhere. As with benefits, these can be slotted into the frameworks already set out above.

User

Consider the marginal user (9)
Collaborate with humans – rather than displace them (5)
Marginal user and participation (10)
Address job displacement implications (8)

robotethics.co.uk comment on user: This is mainly about the social implications of AI and the risks to individuals in relation to jobs and becoming marginalised. These implications seem likely to arise in the short to medium term and given their potential scale, there seems a comparative paucity of attention being paid to them by governments, especially in the UK where Brexit dominates the political agenda. Little attempt seems to be being made to consider the significance of AI in relation to the more habitual political concerns of migration and trade.

AI Industry

AI researchers <-> policymakers (1)
Establish industry partnerships (9)
Responsibility of designers and builders for moral implications (1, 5)
Establish industry partnerships (9)
Culture of trust and transparency between researchers and developers (1)
Resist the ‘race’ – no more ‘move fast and break things’ mentality (1)

robotethics.co.uk comment on AI industry: The industry players that are building AI products and services have a pivotal role to play in their ethical development and deployment. In addition to design and manufacture, this affects education and training, regulation and monitoring of the development of AI systems, their marketing and constraints on their use. AI is likely to be used throughout the supply chain of other products and services and AI components will become increasingly integrated with each other into more and more powerful systems. The need to create policy, regulate, certify, train and licence the industry creating AI products and services needs to be addressed more urgently given the pace of technological development.

Public dialogue

Engage – opening up such work to broader deliberation in an inclusive way (4)
Education and awareness of public (7)
Be alert to public perceptions (3)

robotethics.co.uk comment on public dialogue: At present, public debate on AI is often focussed on the activities of the big players and their high profile products such as Amazon Echo, Google Home, and Apple’s Siri. These give clues as to some of the ethical issues that require public attention, but there is a lot more AI development going on in the background. Given the potentially large and fast pace of societal impacts of AI, there needs to be greater public awareness and debate, not least so that society can be prepared and adjust other systems (such as taxation, benefits, universal income etc.) to absorb the impacts.

Interface design

Representation of AI system, user interface design (10)

robotethics.co.uk comment on interface design: With AIs capable of machine learning they are developing knowledge and skills in similar ways to how people do, and just like people, they often cannot explain how they do things or arrive at some judgement or decision. The ways in which people and AIs will interface and interact is as complex a topic as how people interact with each other. Can we ever know what another person is really thinking or whether the image they present of themselves is accurate. If AIs become even half as complex as people, able to integrate knowledge and skills from many different sources, able to express (if not actually feel) emotions, able to reason with super-human logic, able to communicate instantaneously with other AIs, there is no knowing how people and AIs will ‘interface’. Just as with computers that have become both tools for people to use and constraints on human activity (‘I’m sorry but the computer will not let me do that’) the relationships will be complex, especially as computer components become implanted in the human body and not just carried on the wrist. It seems more likely that the relationship will be cooperative rather than competitive or one in which AIs come to dominate.

The original source material from Josie, (who gave me permission to reference this material) can be found at:

https://docs.google.com/document/d/1LrBk-LOEu4LwnyUg8i5oN3ZKjl55aDpL6l1BxVcHIi8/edit

See other work by Josie Young: https://methods.co.uk/blog/different-ai-terms-actually-mean/