Introduction
With increasingly growing and social uses of artificial intelligence (AI) systems, it remains a significant concern to ensure that it’s made fairly and in a transparent manner. AI ethical considerations can be defined as the regulation, standards, norms and code of conducts in the development of AI technologies. This article carries out an analysis of ethical AI for performance enhancement, aiming at defining what fairness and transparency signify in this context, the difficulties that exist, and the ways through which such values can be advanced.
The Ethical Aspects of Artificial Intelligence
Bias and discrimination are among the most significant ethical threats and challenges arising from AI, and, indeed, society. AI systems accumulate information and act based on it, meaning they reflect the information they have – and if that information is bigoted, then the AI will either reinforce the prejudice or even amplify it. Criminalization of this can lead to discrimination in jobs, credit, policing and health services, among others.
For instance, Amazon’s facial recognition technology was found to have margin higher errors in detecting individuals with darker skin. Likewise AI used in recruiting personnel has been established to have disproportional representation of male candidates relative to females because the tools were trained on biased datasets. Achieving fairness in AI involves tackling these biases when the AI is designed, implemented, and used in roles such as feature selection, model training, and model deployment.
Transparency in AI is one of the ability of machine’s decision making to comprehend by humans. it is important for the ability of users and other stakeholders to audit and even contest the AI decisions made by these systems. One of the problems of AI is the fact that it is sometimes challenging to establish the fairness and ethic of the actions of the system in question when the process is not transparent.
For instance, if an applicant’s loan application has been rejected by an AI system, the applicant should have a right to know why that was the outcome. This makes it necessary for the AI system to give reports on its functioning in form of easily understandable natural language.
Thus, an increasing role of AI in different domains and spheres of life raises the issue of fairness and transparency of its development and application. Business ethics pertains to the set of norms enacted in the deployment and development of artificial intelligence. This article discusses the key issues of ethical AI and the approach to the required fairness and transparency, the problems associated with it, and the ways it can be solved.
Ethical AI: Why does it Matter?
Addressing Bias and DiscriminationThis is perhaps one of the most critical ethical issues that are connected with Artificial Intelligence and other related technologies. AI systems learn from data and if this data already has bias, then the AI implements this bias, or even amplifies it. This may result in discrimination situations within employment, credit, policing, and treatment among other areas.
For instance, the false identification by the smartphones facial recognition technology was proved to be higher in case of the people with the dark skin complexions. Likewise, varied recruitment tools based on AI have been determined to show male bias regarding women candidates because of prejudicial data. it has been noted that to considerably eliminate biases in the conclusions drawn by an algorithm and be as fair as possible across the whole of AI’s lifecycle the noted sources of bias have to be regulated across the noted phases of AI’s lifecycle such as data acquisition, preprocessing, feature selection, data cleansing as well as model building, validation and assessment.
Ensuring Accountability and Transparency
Transparency in AI or explainable AI relates to making the decision-making of AI systems comprehensible to people. This is important in the sense that decisions made by AI can be reviewed and questions asked on them by the users as well as other stakeholders. This is because it becomes hard to understand the process through which an AI system arrives at a certain decision or recommendation or even the fairness of the same system.
For instance, if an AI system refuses to issue a loan, the applicant should be provided with reasons why the request was turned down. This entails the AI system to give understandable and intelligible reasons of its choice or recommended action.
As far as common approaches are concerned, the following strategies can be recommended as the most effective tools of ensuring the concept of fairness and complete transparency:
Diverse and Representative Data
To address this issue, it is required to collect and employ various and balanced sets of data for AI’s training. This entails gathering data from a large number of sources and then making sure that the demographic characteristics of the data are also broad.
Explainable AI (XAI)
In the following context, XAI is defined as approaches and solutions designed to make AI models easier to explain and understand by people. Through the detailing of the rationale of the AI decision making process, XAI helps in increasing the level of transparency and increased accountability. Some of the examples of XAI techniques are decision trees, rules and specifications, and attention in the architecture of Deep learning models.
Specifically identified are ethical audits with the intention of evaluating and assessing the impact of ethical policies and decisions.
Ethical audits and impact assessments can be conducted to ensure any possible ethical problems are spotted with regard to AI systems. These assessments include the assessment of the fairness, openness, and privacy of the AI technologies and proposing changes where deemed relevant.
Healthcare: Ibm–Watson for Oncology
The IBM Watson for Oncology is an AI application that is meant to help oncologists in their work involving the diagnose and treatment of cancer. The implemented machine learning algorithms are applied to analyze patients’ data and offer tailored treatments with reference to modern clinical evidence. To ensure fairness and transparency, IBM Watson for Oncology incorporates several ethical practices:To ensure fairness and transparency, IBM Watson for Oncology incorporates several ethical practices:
Diverse and Representative Data
To address this issue, it is required to collect and employ various and balanced sets of data for AI’s training. This entails gathering data from a large number of sources and then making sure that the demographic characteristics of the data are also broad.
Explainable AI (XAI)
In the following context, XAI is defined as approaches and solutions designed to make AI models easier to explain and understand by people. Through the detailing of the rationale of the AI decision making process, XAI helps in increasing the level of transparency and increased accountability. Some of the examples of XAI techniques are decision trees, rules and specifications, and attention in the architecture of Deep learning models.
Specifically identified are ethical audits with the intention of evaluating and assessing the impact of ethical policies and decisions.
Ethical audits and impact assessments can be conducted to ensure any possible ethical problems are spotted with regard to AI systems. These assessments include the assessment of the fairness, openness, and privacy of the AI technologies and proposing changes where deemed relevant.
Regulatory Frameworks
Therefore there is the need to come up with policies regarding the use of AI to avoid the ignoring of moral standards. To develop a set of general principles to regulate such technologies, policymakers need to coordinate with specialists in the field, academics, and civil society organizations.Continuous Monitoring and Evaluation
The prevention of bias and the creation of fair AI is a process that can take place in increments and needs to be regularly updated. Honest and objective evaluation in this case means a constant check on the AI system for possible biases or ethical issues that may result from the operation of the system. It also means revising the models and the algorithms to incorporate new data and new values such as those aligned with the current culture.
Case Studies: Ethical Artificial Intelligence
Healthcare: Ibm–Watson for OncologyThe IBM Watson for Oncology is an AI application that is meant to help oncologists in their work involving the diagnose and treatment of cancer. The implemented machine learning algorithms are applied to analyze patients’ data and offer tailored treatments with reference to modern clinical evidence. To ensure fairness and transparency, IBM Watson for Oncology incorporates several ethical practices:To ensure fairness and transparency, IBM Watson for Oncology incorporates several ethical practices:
Data Quality: It is a fact that to avoid biases within the system, it is trained on datasets that are as diversified as possible.
Explainability: In the case of Watson, the reasoning behind the chosen strategies and approaches is ultimately clear to the oncologists, as it gives complete explanations regarding its actions.
Privacy: Privacy of the patients is maintained in the analysis by adopting strategies such as anonymizing the patient data and encrypting the data.
Law Enforcement: Predictive Policing
Predictive policing entails incorporating of predictive analysis to the crime data to determine areas where most crimes are likely to happen. Though it is crucial in increasing security to the public to some extent, this technology is associated with serious ethical dilemmas, especially in matters to do with bias and transparency.
Several police departments in the United States have implemented measures to address these concerns:Several police departments in the United States have implemented measures to address these concerns:
Bias Mitigation: The algorithms themselves are frequently reviewed in order to remove any prejudice the may tend to profile certain groups of people.
Transparency: To ensure that the technology being used in police work is transparent, police departments are forced to explain to people how the predictive policing algorithms work and why certain decisions are being made.
Community Engagement: Police departments use community policing to create goodwill so as to gain the support of the members of the community when implementing and using predictive policing technologies.
Recruitment: Seemingly, the method applied by Unilever is an example of the SF Estimate method within the contextual framework of the AI-driven hiring process.
Large multinational company Unilever has also started using artificial intelligence in the hiring process to eliminate bias. The video interviews of the candidates are processed using artificial intelligence algorithms to identify the answers of the candidate and their disposition as well.
To ensure fairness and transparency, Unilever has adopted the following practices:To ensure fairness and transparency, Unilever has adopted the following practices:
Diverse Data: Bias is avoided when choosing the evaluation data, which the AI system processes, as the evaluating data consist of a diverse pool.
Explainability: This is accompanied by post-competition feedback and explanation of decision-making process that was used by the AI system to review the candidates’ responses.
Ethical Oversight: Unilever engages in ethical audits frequently in order to check the impact that their AI-based recruitment has on the applicants.
The presentation will focus on the future of Ethical AI.
Improvements in presentation of matters to avoid bias and favoritism
There is growing awareness and important work being done in the fairness and bias issues as the AI technology expands. Currently, scholars propose several approaches to mitigate bias in AI models, for example, the method of fairly splitting a dataset between two machine learning models or just debiasing algorithms.
Explainability: In the case of Watson, the reasoning behind the chosen strategies and approaches is ultimately clear to the oncologists, as it gives complete explanations regarding its actions.
Privacy: Privacy of the patients is maintained in the analysis by adopting strategies such as anonymizing the patient data and encrypting the data.
Law Enforcement: Predictive Policing
Predictive policing entails incorporating of predictive analysis to the crime data to determine areas where most crimes are likely to happen. Though it is crucial in increasing security to the public to some extent, this technology is associated with serious ethical dilemmas, especially in matters to do with bias and transparency.
Several police departments in the United States have implemented measures to address these concerns:Several police departments in the United States have implemented measures to address these concerns:
Bias Mitigation: The algorithms themselves are frequently reviewed in order to remove any prejudice the may tend to profile certain groups of people.
Transparency: To ensure that the technology being used in police work is transparent, police departments are forced to explain to people how the predictive policing algorithms work and why certain decisions are being made.
Community Engagement: Police departments use community policing to create goodwill so as to gain the support of the members of the community when implementing and using predictive policing technologies.
Recruitment: Seemingly, the method applied by Unilever is an example of the SF Estimate method within the contextual framework of the AI-driven hiring process.
Large multinational company Unilever has also started using artificial intelligence in the hiring process to eliminate bias. The video interviews of the candidates are processed using artificial intelligence algorithms to identify the answers of the candidate and their disposition as well.
To ensure fairness and transparency, Unilever has adopted the following practices:To ensure fairness and transparency, Unilever has adopted the following practices:
Diverse Data: Bias is avoided when choosing the evaluation data, which the AI system processes, as the evaluating data consist of a diverse pool.
Explainability: This is accompanied by post-competition feedback and explanation of decision-making process that was used by the AI system to review the candidates’ responses.
Ethical Oversight: Unilever engages in ethical audits frequently in order to check the impact that their AI-based recruitment has on the applicants.
The presentation will focus on the future of Ethical AI.
Improvements in presentation of matters to avoid bias and favoritism
There is growing awareness and important work being done in the fairness and bias issues as the AI technology expands. Currently, scholars propose several approaches to mitigate bias in AI models, for example, the method of fairly splitting a dataset between two machine learning models or just debiasing algorithms.
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