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• Countercheck and validate all the assumptions taken and the results.
• Keep a watch on the functioning and working of your system, as something can go wrong and timely detection
of the same is important.
• Also, fix the responsibility and set the accountability.
Ethics and Personal Data
Ethics play a crucial role in handling personal data, focusing on privacy, consent, transparency, and data security.
Privacy ensures that individuals' personal information is respected and protected, requiring organisations to
collect, use, share, and process data in ways that maintain confidentiality. Consent involves obtaining clear and
explicit permission from individuals before collecting, sharing, processing, or using their data, ensuring they are
informed about how their data will be used and giving them the option to withdraw consent. Transparency means
being open about data collection practices, clearly communicating what data is collected, how it is used, stored
and analysed, and who it is shared with. Data security involves implementing strong measures to protect personal
data from unauthorised access, breaches, and other threats, ensuring the integrity and safety of the information.
These ethical principles help build trust and ensure responsible data management.
What are the Principles of AI Ethics?
Ethics in AI encompasses the moral principles, values, and guidelines that govern the development, deployment,
and use of artificial intelligence systems.
• Human rights: This principle emphasises that AI solutions should respect, protect, and uphold fundamental
human rights. This includes rights such as privacy, freedom of expression, freedom from discrimination, and the
right to a fair trial. AI systems should be designed and implemented in a way that they do not infringe upon
these rights and should be held accountable if they do.
• Bias: Bias in AI refers to the unfair or unjust treatment of individuals or groups based on characteristics such
as race, gender, age, or socioeconomic status. Bias can be unintentionally introduced into AI systems through
biased training data, flawed algorithms, or skewed decision-making processes. Addressing bias in AI involves
identifying, mitigating, and preventing bias at every stage of the AI development lifecycle, from data collection
and preprocessing to model training and deployment.
• Privacy: Privacy concerns the protection of individuals' personal data and their right to control how that data
is collected, used, and shared. AI systems often rely on vast amounts of data, which may include sensitive
information about individuals. It is essential to implement robust privacy measures, such as data anonymisation,
encryption, and user consent mechanisms, to ensure that AI solutions respect individuals' privacy rights and
comply with relevant data protection regulations.
• Inclusion: Inclusion in AI refers to ensuring that AI solutions are accessible, equitable, and beneficial for all
members of society, regardless of factors, such as race, gender, disability, or socioeconomic status. This involves
considering the diverse needs, perspectives, and experiences of different user groups throughout the design,
development, and deployment of AI systems. Inclusive AI design aims to prevent the exacerbation of existing
inequalities and to promote equal opportunities and outcomes for all individuals.
By adhering to these AI ethics principles, developers and organisations can contribute to the creation of AI solutions
that are not only technically robust but also ethically sound, socially responsible, and aligned with the values and
interests of society.
AI Ethical Issues and Concerns
As Artificial Intelligence evolves, so do the issues and concerns around it. Let us review some of the issues and
concerns around AI.
AI Reflection, Project Cycle and Ethics 139

