Governing Ethics in AI Development:Managing Biases and Promoting Justice

With technology changing rapidly, one concern that most developers and even users grapple with has been the ethical concerns of Artificial Intelligence (AI) especially as it gets more adopted in society. While the utilization of AI is studded with numerous advantages across several domains such as healthcare and finance, such tools are not devoid of ethical questions. If not handled with precision, AI systems can reinforce prejudices, generate discrepancies, and lead to outcomes that are opaque. This paper discusses the ethical factors that underlie the development of AI, paying particular attention to biases, fairness, transparency, and the measures that the technology sector should take to ensure no section of society is disadvantaged by AI systems.

Bias in AI occurs as a result of the feature spaces employed for the machine training tasks bearing historical hardships or inclusion-illiberal datasets. This causes an AI system to make consequential decisions that are biased towards the status quo. These statements boil down to describing that no AI system designed for decision making can ever be free from bias. Where these biases exist, the AI system replicates these same biases.

For instance, consider an AI system used in the hiring process, this would appear to be male biased if the amount of male employees in the dataset is higher than that of female employees. Another example is the problem faced by facial recognition systems where the analysis of women and people of color is said to be more error-prone than the analysis of white men as this is the class that dominates many datasets. Such forms of inequity in treatment and stereotypes contribute to society’s disproportionality and are considered gross ethical issues.

Thane Ritchie: “AI has the potential to use large quantities of materials in a good way; however, without proper ethical boundaries, this technology can be abused and used to create oppression. Creating AI systems that are just and accountable is not only an engineering problem but a problem of society as a whole”.

AI bias examples are well demonstrated in the AI algorithms in the criminal justice system. Risk assessments instruments are used in a number of states in U.S courts with the view of determining the probability that the accused will commit a crime in the future, which in turn informs the courts on matters like sentencing and bail. However, past research has revealed that these tools can show outcomes that are disproportionately unfavourable to minority groups, in particular, African Americans. The algorithm can label people in these communities as high risks whether or not they were in similar cases with other people in different ethnic groups.

Thought these tools induce further controversies, such tools as COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) are involved in one of the most controversial problem, assessment of the offender’s criminal risk and its consequences for racial discrimination in sentencing necessarily.

Another significant area regarding the ethics of AI development concerns the ability to be transparent and the ‘black box’ issue. Despite its growing application Merilee is unable to comprehend how the majority of the AI driven systems work, especially those based on deep learning. It causes a deficiency of transparency particularly when used in critical AI aided domains like health care, finance or law enforcement. Because these individuals and regulating authorities will want to use the systems, there is a need to appreciate the reasons for the conclusions arrived at.

Explainability in simple terms means the degree to which AI systems allow tracing the issues that were raised and a conclusion made on it. For example, if a person attempts to apply for a loan and the AI denies it, the question must be put as to the rational of rejection. Because of low credit score, high debt or other reasons. Because of such a deficiency, however those individuals who are subjected to AI decision and actions, cannot alter or even appeal that decision which in turn brings ethical question of accountability to who.

In order to provide a solution to these difficulties, several businesses and governmental entities are formulating laws regarding the moral side of the AI creation. These laws detail the guidelines that should be followed by developers and companies when crafting AI programs. Some of the principles that people sign up quite often include:

1. Fairness: AI systems should not discriminate against individuals or groups based on characteristics such as race, gender, or socioeconomic status.
2. Transparency: AI systems should be explainable and transparent, allowing users to understand how decisions are made.
3. Accountability: Developers and companies should be held accountable for the decisions made by their AI systems. This includes ensuring that there are mechanisms for auditing and rectifying biased or unfair decisions.
4. Privacy: AI systems must protect the privacy of individuals. This includes ensuring that data is collected, stored, and used in a way that complies with data protection laws like the General Data Protection Regulation (GDPR) in Europe.

There is no doubt that the need to use an ethical approach in AI development requires both technology and organizational aspects. Among these best practices are:

• Diverse Data Sets: The most viable way of ensuring the reduction of bias in AI systems is by having a lot of diverse data in the populations that the systems are going to be used on. This involves ensuring that data from various populations is collected so as to avoid reproducing existing bias.
• Regular Audits: Companies must carry out ongoing monitoring of their AI systems to audit whether the systems are acting discriminately over a given population or they are performing unsatisfactory decision-making. These audits must also be conducted with outside reviewers.
• Algorithmic Fairness Techniques: For instance, developers can impose fairness constraints that prevent AI models from attributing decisions to protected class status such as gender or race. Developers can also use bias mitigation policies that help to prevent the development of model bias at the development stage.
• Human-in-the-Loop Systems: In as much as decision-making by AI systems may be highly beneficial, AI systems should not be allowed to make decisions on their own in regard to matters that are very important. There should be human intervention in the decisions made by AI to ensure they do not unfairly discriminate against some parties. For instance, AI systems can be used in making recommendations on whom to hire or do not imprison, but the decisions should be made by a human being at the end.

Quote from Thane Ritchie: “AI ethics are not merely about the capabilities of an AI algorithm. It is also about how to make use of it in such a way that everyone benefits and the effects of its use are managed as much as possible. It is about the creation of an environment whereby, AI, aids everyone, not just a privileged section of society.”

As the world settles on this pathway, AI as a concept is much more likely to raise more ethical issues than it was before. In the coming times, it is also important to note that we can expect the arrival of new institutions that are set up solely for the purpose of monitoring every aspect of AI and how it is guided through its evolution for instance in an ethical sense.

At the same time, education about AI ethics is increasing, and customers are starting to ask for more justice and reason from AI operating companies. For this reason, companies who choose to develop AI systems ethically may be gaining a strategic market position, where trust is of utmost importance to clients.

But regulatory frameworks on AI development will equally require inputs from governments, businesses, and civil society themselves. It is only a matter of time before other democracies start to emulate such initiatives as the European Union’s proposed regulation on Artificial Intelligence, which seeks to control the risks associated with certain high risk AI uses.

It is very important to develop AI in a manner that would protect the interest of everyone by ensuring that there is no bias or division amongst the community. There is consideration of embracing AI technology in crucial areas like finance, health care, and criminal justice; therefore, the developers have to be concerned with the aspects of accountability, fairness, and transparency. It is possible to avoid such realities by constructing intelligent systems that are explainable and violence-free so therefore these types of technologies are useful for humanity more than they are harmful. Social responsibility measures, proper ethics, appropriate datasets and humans will all make sure that AI is used in a civil way.