Artificial Intelligence (AI) provides the mechanisms to enable machines to learn. AI systems which use machine learning, can detect patterns in enormous volumes of data and model complex, interdependent systems to generate outcomes that improve the efficiency of decision making. Machine learning is also seen as a form of applied statistics, albeit with an increased use of computing and data that is used to statistically estimate complicated functions. Machine learning depends on learning from patterns of data to make new predictions.

AI in particular machine learning has been positioned as an important element in contributing to as well as providing decisions in a multitude of industries. The use of machine learning in delivering decisions is based on the data that is used to train the machine learning algorithms. It is imperative that when machine learning applications are being considered that the data being used to train the machine learning algorithms are without bias, and the data is ethically used. AI Ethics is the responsible and trustworthy design, development, implementation and use of AI systems including the data used to train the AI systems and the knowledge produced by them.

Ensuring that AI systems are developed and used in a way that promotes equality and fairness for the users and those effected by the AI system should be at the forefront of any AI system implementation as well as its ethical use and the ethical use of data. To ensure AI systems are developed with an ethical core, it is essential to start with establishing a diverse AI product development team that is active in the design, development, and implementation of the AI application. A diverse team will bring a “diversity of thought” to the initiative and especially during the selection and cleansing of data to assist in removing bias from being a part of the algorithms being used and ensure the models are trained with ethical data that adheres to data privacy and security. A diverse team, through collaboration, knowledge sharing, and knowledge reuse will bring different points of view, different experiences, and different cultural backgrounds to stimulate innovation and to eliminate (or limit) bias. This action leads to innovation. This innovation will enable organizations to deliver unique and or improved AI products.

Leaders also need to aware of the ethicality of AI applications being developed and deployed at their organizations. Leaders must examine and understand whether the outcomes from the application of AI violate US Federal, GDPR, and/or other ethical, security, and privacy standards. Leadership will need to adopt a standard for AI that identifies general tenants for AI implementation focused on ethical adherence[1]. Leaders must enable support for implementation, acceptance, and adoption of AI. Considerations for cultivating a system thinking mindset and incorporating the five disciplines of systems thinking, personal mastery, creation of mental models, creation of a shared vision and cultivation of team learning[2], is essential for effective leadership of AI implementation.

The ethical use of data in AI applications is a critical issue as AI systems and algorithms rely on data to learn and make decisions. The way data is collected, stored, used, and shared can have significant impacts on individuals, organizations, and society. Ethical use of data in AI systems build trust and ensures that they are adopted and used in a responsible manner. This chapter exams AI bias and ethical use of AI Applications, Data Ethics Principles, selecting ethical data for AI applications, AI and Data Governance, and Putting Ethical AI Applications into Practice.

[1] Rhem, A.J. (2021). AI ethics and its impact on knowledge management. AI Ethics 1, 33–37. https://doi.org/10.1007/s43681-020-00015-2

[2] Senge, P. (2006). The Fifth Discipline: The art and practice of the learning organization. Random House Books.

(Note: To access the full chapter go to: Ethical use of data in AI Applications. This chapter will be included in the book: Ethics in Scientific Research – New Perspectives)

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