Four principles of Responsible Artificial Intelligence
Overview –
As AI becomes all-pervading, it needs to be more responsible and ethical. AI without these boundaries can cause massive problems. However, the task is not easy. First, all stakeholders must stop treating responsible AI and ethical AI as interchangeable terminologies and clarify their definitions and scope. Second, organizations need to balance their obligations of complying with corporate governance and responsible AI principles. Third, the stakeholders must define common and unique principles for responsible AI because the rules and principles will vary across industries. Fourth, an AI system must be developed around a standard framework. Recently, a few experts on responsible AI got together to discuss the issue of incorporating responsible AI principles in AI systems, and the discussion produced some great ideas that could help set a kind of template that AI systems could follow. The experts were Anthony Habayeb, co-Founder and CEO of Monitaur, an AI governance and assurance software company, Andrew Perry, AI ethics evangelist, and Alyssa Lefaivre Škopac of the Responsible AI Institute.
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Set the definition and scope of responsible and ethical AI
Responsible AI and ethical AI are being treated interchangeably which is not correct and that can potentially jeopardize the task of creating a reliable AI ecosystem. While ethical AI is about the various aspirational values such as human individuality, recognition of human rights, enabling autonomy, and fair outcomes, responsible AI is a set of technological and organizational measures that enable us to achieve those aspirational objectives. The sum total of these two systems can also be called trustworthy AI. Clarity is the first step to creating a fair, logical, and equitable AI ecosystem.
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Set the balance between responsible AI and corporate governance
Organizations must follow corporate governance principles such as balancing the interests of the company’s shareholders, customers, community, financiers, suppliers, government, and management. That makes incorporating and executing responsible AI systems a difficult task. There are both different and overlapping principles such as the following:
- Clarity and transparency in decision-making processes
- Demonstrating accountability in actions and decisions toward all stakeholders
- Careful considerations of the ethical implications of the actions of the organizations and making decisions that align with ethics and values.
- Conflicts between interests. A responsible AI system must be equipped to handle conflicts of interest between the shareholders and the customers. For example, the Volkswagen incident is an ideal case in point. The corporates may want to reward the shareholders at the expense of the public or the customers which could be a dangerous intention. AI systems should be able to provide objective data that could aid transparent investigations if such conflict of interests ever occur in the corporate or government sector.
Organizations need to find synergy by combining responsible AI and corporate governance.
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Debate the ethical issues that should govern the AI system
An AI system, irrespective of the industry, must manage disparate ethical issues. The company’s reputation and public perception depend on the ways the issues are handled. First, the lending system of financial institutions is a case in point. Many financial institutions use AI to evaluate applications for loans or mortgages. However, the AI systems are fed with generic or stereotypical data that results in massive denials of applications from the African American community members whose Fair Isaac Corporation (FICO) credit scores have been low. So, lending AI systems need to be able to evaluate loan applications purely based on merit and not based on race or community considerations. Second, the ecological and environmental impact of AI systems must be discussed. Some research shows that a single training AI system can emit as much as £150,000 of carbon dioxide. So, there needs to be a balance between development and its impact on the environment. Third, unlike in the case of a human being, the decisions by AI systems and their outcomes must be evaluated objectively, based on data and context. We have the opportunity to evaluate decisions more objectively. Fourth, AI systems must be protected from cybercrimes or hacking. AI systems are going to be massively empowered with humongous volumes of data. All AI systems, by their nature, are going to guzzle vast volumes of data every day through machine learning and that is a goldmine for hackers. So, AI systems must be protected from malicious actors.
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Follow a proven framework to develop a responsible AI system
Organizations such as the Institute of Electrical and Electronics Engineers (IEEE), the European Commission(EU), and the Partnership on AI have already been developing a framework to develop responsible AI systems. The framework is based on the following principles, largely:
- Objective and quantifiable parameters of ethics. For example, an AI medical system should be able to accurately diagnose the medical conditions of patients and prescribe custom remedies without regarding the billing aspect.
- AI systems must apply the same evaluation, assessment, and judgment parameters regardless of scenarios or people. For example, financial institutions that are using AI systems to evaluate loan applications must apply the same parameters to all applicants irrespective of their race or background.
- Privacy and safety. AI systems must stringently safeguard confidential data. For example, medical AI systems must safeguard patient data to prevent patients from falling victim to scams. An extremely sensitive case in point is defense data whose breach can lead to catastrophic consequences.
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Conclusion
The importance of responsible AI is beyond debate but the task of establishing one is long winding and rather complex. The complexity increases because the systems must cater to diverse ecosystems, and people from diverse backgrounds, races, and communities. The developers of AI systems must take into account diverse factors that are related to different industries. For example, the factors related to the medical care industry will be different from those in the civil aviation industry. However, certain standard principles and parameters-such as confidentiality, fairness, and transparency-will be common across the industry. Responsible AI is still a nascent idea but developing fast. All stakeholders have been finding their way through the idea and the future seems exciting.
