Human Capital & Strategy-III

Navigating the Ethical Maze: Ensuring AI Fairness, Accountability, Transparency, and Privacy

Technology has reached a point where it needs guidance in decision-making. Ethical AI guidelines support the development of inclusive AI with fairness and accountability qualities. -By Joseph Warren

Artificial intelligence (AI) is becoming more pervasive every day. It is used across industries to automate administrative work, support inclusive talent management systems, produce predictions to guide organizational leadership decision-making, and much more. With technological advancements and widespread use comes a responsibility to develop and maintain ethical, fair, accountable, transparent AI systems while protecting privacy. Each organization needs an ethical AI framework that serves as a compass. Ethical standards and guidelines also ensure the organization aligns its AI projects with social values.

Global Recognition of the Need to Protect Human Rights

,p>AI seems to get more powerful with each iteration. It can have enormous influence in many ways because it has reached a stage where it makes many decisions about workflows, supply chain participation, marketing targets, talent recruitment, and applicant screening. Some international organizations have developed AI ethics frameworks, realizing that AI can do harm as well as good if not developed with clear standards for fairness and minimizing bias and discrimination.

Some organizations that have developed model AI frameworks include the European Commission’s High-Level Expert Group on Artificial Intelligence, the IEEE, the OECD, and UNESCO. UNESCO produced the first global standard on AI ethics, which was adopted by all member states. Protecting human rights and dignity is the foundation driving the incorporation of principles like fairness and transparency. The four core values of the UNESCO Recommendation are human rights and human dignity; living in peaceful, just, and interconnected societies; ensuring diversity and inclusiveness; and environment and ecosystem flourishing. The Recommendation is available online and was developed to “guide the actions of individuals, groups, communities, institutions and private sector companies to ensure the embedding of ethics in all stages of the AI system life cycle.”

Machine Ethics: New Field of Endeavor

AI ethics concerns AI's principles, guidelines, policies, rules, and regulations and how AI upholds the ethics norms. Ethics for AI are the same as ethics for any organizational process or system. The difference is that AI is a complex technology that produces outcomes that can impact people’s lives, and building ethics into AI and monitoring AI behavior is the purview of AI engineers and developers. Most people do not understand the software development process. They leave it to the specialists and experts, becoming concerned only when something harmful happens, such as Amazon’s applicant screening excluding people of color or a bank refusing loan applications due to AI bias.

The guidelines are necessary because they define expectations for AI ethics. The IEEE defines the categories of vulnerabilities of AI and humans. One ethical issue is that AI machine learning is data-hungry, so it motivates companies to purchase or collect data that may violate people’s right to privacy. Another ethical issue is garbage in-garbage out data using training datasets. Additional issues include faulty algorithms that find patterns that do not exist and a lack of explainability and trust in deep learning because of its millions of connections.

AI ethics also apply to humans using AI. For example, there is some intentional abuse of AI systems, and the fear some employees have that AI will replace their jobs. Ethical issues are related to algorithms, like security, explainability, algorithmic decision dilemmas, data concerning privacy, use of sensitive personal information, discrimination, and so much more.

Monitoring AI Ethics

Recognizing the principles of a just AI system is the first step. The next is embedding the principles in the AI lifecycle and monitoring results. IBM’s AI Fairness 360 is an open-source toolkit that assists organizations with identifying, reporting, and mitigating discrimination and bias in the machine learning models through the AI lifecycle. It is a comprehensive set of metrics for datasets and models to test for biases and algorithms to mitigate bias in datasets and models. IBM is developing additional tools in its Trustworthy AI initiative that address various AI ethical issues.

Of course, keeping up with the rapid pace of change in AI is not easy. For example, generative AI produces false images that can harm people, and chatbots behave in bullying ways. IBM is working on various technology tools that make it easier to prevent, identify, or mitigate AI programs causing harm and to increase “fairness, accountability, transparency.”

Ethical guidelines promote AI systems designed and trained to be fair and unbiased and avoid discrimination against any individual or group based on characteristics such as race, gender, ethnicity, religion, sexual orientation, or disability. Organizations need to implement techniques such as bias detection and mitigation throughout the AI lifecycle, from data collection and preprocessing to model development and deployment. They also must regularly audit AI systems for biases and unintended consequences and take corrective actions as necessary.

Getting Practical

The technology company Transcend offers an AI privacy platform to speak directly to AI privacy as a primary ethical concern. The company makes the point that translating principles of ethics into guidelines means integrating ethics at every stage of the AI lifecycle, from conceptualization to ongoing monitoring. The guidelines address each stage. For example, data is ethically sourced, performance is monitored for bias, AI decisions are documented, accessible and understandable, and an accountability framework exists.

Ethics do not change, but ethical challenges continue to grow. In any organization, there is never any certainty that technology will not be misused. That explains why ethical considerations include continuous testing and monitoring of output. The ethics guidelines give organizational leaders and auditors clear direction on what to look for in AI data, outcomes, and information use.

To stay abreast of evolving ethical considerations, best practices, and regulatory requirements, continuous learning and improvement should occur within organizations and the broader AI community. Organizations need to regularly review and update AI ethics guidelines and practices in response to new insights, experiences, and stakeholder feedback. They also need to invest in research and development efforts to advance state-of-the-art ethical AI and develop innovative solutions to ethical challenges.