By CFA Institute has developed a framework that incorporates fundamental ethical principles and relevant professional standards into the design, production and deployment of AI in investment management. These include preservation of human autonomy, prevention of harm, fairness, explicability and privacy.
What Ethical Considerations Surround AI Trading?
A primary ethical concern is that ML/AI algorithms may reproduce existing biases. This is often illustrated in high-profile cases where gender or racial biases are replicated by machine learning systems. For insights into the cutting-edge AI trading landscape, a visit to Quantum AI’s official website is a valuable resource.
Legal
The list of ethical concerns relating to AI is diverse and growing. It includes issues that are both theoretical and practical. Many are linked to questions of regulation and legislation. This suggests that they cannot be seen as separate from societal decisions on how to structure and use large socio-technical systems.
For example, the opacity and unpredictability of many AI technologies can result in bias and discrimination. This is because these systems learn based on data, and if that data is biased in some way, they can perpetuate or amplify existing biases.
Furthermore, the decision-making processes of these systems may create new types of moral issues that have not previously existed. This could include the potential for companies to generate profits in ways that are unethical or illegal, such as through the exploitation of personal data. In addition, it may lead to issues around the fairness of certain outcomes or the distribution of wealth and power.
Privacy
As with many new technologies, there are both benefits and risks associated with the use of AI. On the one hand, it can enable access to spaces for action that were previously inaccessible, such as allowing partially sighted drivers to operate vehicles autonomously or creating personalised medical solutions beyond what is possible currently.
On the other hand, AI may lead to a loss of privacy. For example, a medical researcher might inadvertently reveal sensitive information about patients, or a consumer brand could expose its product strategy to competitors. Such incidents can be damaging to customer or patient trust and carry legal ramifications.
These concerns arise largely due to the opacity and unpredictability of current AI technologies. For instance, a black-box algorithm trained on historical data can perpetuate intrinsic biases. The system can also be manipulated by hostile actors. This is particularly worrying in healthcare, where even minor changes can have profound consequences.
Markets
There are a variety of ethical concerns that surround AI trading. One is the risk that black box AI algorithms will run amok, causing them to all sell at the same time and triggering a market crash. Another is the risk that AIs will replicate existing biases, such as gender or racial discrimination. There are numerous high-profile examples of such biases being reproduced by machine learning.
Other risks include a need for enormous training datasets, which can lead to model homogeneity and herding, and the fact that the models are opaque to developers, deployers and users, making it difficult to know how they will react to certain inputs.
Regulation
Several ethical issues have arisen due to the opacity and unpredictability of ML/AI technologies. These include the potential for algorithms to produce unfair discrimination and to harm patients, consumers, and individuals. Efforts have been made to mitigate such impacts by developing mathematical notions of fairness. However, these are largely distinct from real-life determinations of fairness, which must be grounded in shared ethical beliefs and values.
Another issue is the concentration of economic (and thereby political) power among large data-driven companies. This problem, which predates AI, could be exacerbated using AI-related technologies.
The development and deployment of AI tools require a substantial investment. As such, firms deploying AI must dedicate personnel and resources to ethics, risk assessment and mitigation programs. These should be monitored, documented, and updated over time to ensure the effective design, testing and application of AI technologies. This is the only way to ensure that AI tools meet the highest ethical standards.
