Page 39 - Banking Finance December 2024

ARTICLE

issues. Many AI applications rely on vast amounts of a sense of openness of the actions and operations
personal data, which raises questions about consent and performed by the machine. As per 'Code of Ethics' , the
data ownership. Users often lack awareness of how their provisions of transparency helps the user or developer
data is collected, stored, and utilized. To safeguard privacy, to understand the actions and decisions taken by the
ethical AI practices must prioritize user consent and data AI internally.
protection, ensuring that individuals retain control over their 2. Data Security Standards: A secure AI leads to integrity,
information.
confidentiality, and availability of AI systems and data.
The 'Code of Ethics' provisions include access control and
What are the Ethical Challenges of AI? authentication mechanisms to enable security. By

Some of the common AI Ethics challenges that are discussed prioritizing security in AI, stakeholders can mitigate
below: risks, safeguard user privacy, and ensure the
1. Opacity: Opacity is a key ethical challenge in AI trustworthiness and reliability of AI systems.
technology, as AI systems often operate as black boxes, 3. Equity and Unbiased Decision-Making: Addressing bias
making it difficult for users and stakeholders to and promoting fairness is essential to ensure that AI
understand how decisions are made or why certain technologies are developed, deployed, and used in an
outcomes are produced. Lack of transparency usually ethical and responsible manner. The 'Code of Ethics'
leads to other challenges such as bias, fairness, etc. emphasizes the importance of mitigating biases in AI
2. Attacks and breaches: AI is prone to adversarial algorithms and data to prevent unfair or discriminatory
attacks and since AI solely relies on data, there is a high outcomes.
scope for cyber-attack leads and data breaches. To 4. Ethical Responsibilities: Responsibility refers to the
prevent these, a secure mechanism is cyber-attack ethical and legal obligations of individuals,
leads required to safeguard the sensitive data and to organizations, and stakeholders involved in the
promote a secure AI. development, deployment, and use of AI technologies.

3. Algorithmic biases: Biases present in training data or It refers to the importance of taking accountability for
algorithmic decision-making processes can result in the outcomes and impacts of AI systems.
unfair or discriminatory outcomes. Such biased data 5. Safety and Well-being: The 'Code of Ethics' emphasizes
leads to underrepresentation or overrepresentation the importance of assessing and mitigating potential
which in turn concludes an unethical AI. risks and hazards associated with AI systems, such as
4. Ethical Accountability: A minor crept or error in an AI system failures, errors, or unintended consequences, to
technology can lead to problems such as biases, minimize harm and ensure safety of AI technologies.
discrimination, privacy violations, and safety hazards,
it is required for a user, stakeholder, deployer or a Steps to Make AI More Ethical
developer to take the responsibility that involves
Here are some key steps to promote ethical AI development
addressing the ethical dilemmas, concerns, and issues
and deployment:
that arise from the development, deployment, and use
1. Develop and Implement Ethical Guidelines:
of AI technologies.
Create clear frameworks outlining expectations for
5. Risk Management: Various risks rise during the responsible AI development and use.
development, deployment of AI such as system failures, These guidelines should address issues like fairness,
errors, or unintended consequences and addressing transparency, and accountability.
these challenges requires careful consideration of safety
risks, robust risk management strategies, and the 2. Mitigate Bias in AI Systems:
implementation of safety measures to promote the safe Ensure training data is high-quality and free from
use of AI technologies. bias.
Employ techniques like debiasing algorithms and
What is the AI Code of Ethics? fairness checks to identify and address potential
biases.
1. Openness and Disclosure: Transparency in AI refers to

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