ΑΙhub.org
 

AI and human autonomy: an analysis of the interaction between intelligent software agents and human users


by
24 January 2020



share this:

Is our autonomy affected by interacting with intelligent machines designed to persuade us? That’s what researchers at the University of Bristol attempted to find out through an analysis of the interaction between intelligent software agents and human users.

Interactions between an intelligent software agent (ISA) and a human user are ubiquitous in everyday situations such as access to information, entertainment, and purchases. In such interactions, the ISA mediates the user’s access to the content, or controls some other aspect of the user experience, and is not designed to be neutral about outcomes of user choices. Like human users, ISAs are driven by goals, make autonomous decisions, and can learn from experience.

A typical ISA, such as a recommender system, might have to select a set of videos for a user to watch (out of a vast catalogue), using any available information or signal it has about the given user (e.g. location, time, past usage, explicit ratings, and much more). In this case, the ISA’s goal is to select an action that, for the given user, maximises the expected click-through rate: an expression of the probability of users clicking through links.

Using ideas from bounded rationality (and deploying concepts from artificial intelligence, behavioural economics, control theory, and game theory), the team frame these interactions as instances of an ISA whose reward depends on actions performed by the user.

The team present a model of an autonomous agent that allows them to distinguish various types of control that actual ISAs can exert on users. The framework of this model allows different types of interaction (i.e. trading, nudging, coercion and deception) to be separated, and presents a unified narrative for discussion of polarisation, addiction, value alignment, autonomy, misuse of proxies for relevance feedback, and moral accountability, as well as other important ethical, psychological and social issues that arise from second-order effects.

This framework is proposed as a resource to better enable philosophers and scientists, policy-makers, and other interested parties, to engage with these issues with a shared conceptual basis. The research highlights the importance of framing the interactions between human users and ISAs as potentially generating positive feedback loops. The nature of the feedback commonly used by learning agents to update their models and subsequent decisions could steer the behaviour of human users away from what benefits them, and in a direction that can undermine autonomy and cause further disparity between actions and goals as exemplified by addictive and compulsive behaviour. ISAs could sometimes exploit and reinforce weaknesses in human beings. It may be possible to mitigate this by using negative feedback, but first, and in any case, the ethical concerns raised in this work must be faced.

Read the full research article:
An Analysis of the Interaction Between Intelligent Software Agents and Human Users Burr, C., Cristianini, N. & Ladyman, J. Minds & Machines (2018).

This work is part of the ERC ThinkBIG project, Principal Investigator Nello Cristianini, University of Bristol.




Nello Cristianini is a Professor of Artificial Intelligence at the University of Bristol.
Nello Cristianini is a Professor of Artificial Intelligence at the University of Bristol.




            AIhub is supported by:


Related posts :



Dataset reveals how Reddit communities are adapting to AI

  25 Apr 2025
Researchers at Cornell Tech have released a dataset extracted from more than 300,000 public Reddit communities.

Interview with Eden Hartman: Investigating social choice problems

  24 Apr 2025
Find out more about research presented at AAAI 2025.

The Machine Ethics podcast: Co-design with Pinar Guvenc

This episode, Ben chats to Pinar Guvenc about co-design, whether AI ready for society and society is ready for AI, what design is, co-creation with AI as a stakeholder, bias in design, small language models, and more.

Why AI can’t take over creative writing

  22 Apr 2025
A large language model tries to generate what a random person who had produced the previous text would produce.

Interview with Amina Mević: Machine learning applied to semiconductor manufacturing

  17 Apr 2025
Find out how Amina is using machine learning to develop an explainable multi-output virtual metrology system.

Images of AI – between fiction and function

“The currently pervasive images of AI make us look somewhere, at the cost of somewhere else.”

Grace Wahba awarded the 2025 International Prize in Statistics

  16 Apr 2025
Her contributions laid the foundation for modern statistical techniques that power machine learning algorithms such as gradient boosting and neural networks.




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association