Science fiction movies like ‘The Terminator’ and ‘I, Robot’ have exhibited what might happen in case artificial intelligence goes rogue. Such dystopian fantasies about AI are widely discussed by experts and researchers in the field of AI as well. Many of these experts believe that super-intelligent AI systems will pose a significant threat to humanity in the near future. And, considering the untold potential of AI, this may soon become a reality.
Developers need to understand public concerns over the development of AI systems. There have been many reported instances where developers neglected these warnings and created AI systems that went rogue. For instance, Microsoft had developed an AI-powered tweet bot that spontaneously started posting offensive and racist tweets. Such AI systems, if used for critical applications, may pose a great threat to humanity. Hence, tech giants and developers must analyze various issues with AI safety with AI and focus on building trustworthy AI systems.
It is highly likely that the negative implications of AI will not be as bad as sci-fi movies and books depict. However, the possible negative consequences of AI can still pose significant threats to the human race. Experts have discussed consequences like losing jobs to AI, where humans may soon be replaced by AI in various roles. The competence of AI can already be witnessed in different industries such as healthcare, retail, aviation, manufacturing, and many more as AI-enabled applications have transformed and streamlined various business procedures. Hence, well-established businesses are leveraging AI for automating core tasks. For instance, Goldman Sachs had replaced almost 600 traders with AI-powered robots. Likewise, a majority of customer service jobs are now handled by AI-enabled chatbots. Hence, people are concerned about AI taking over jobs in just about every industry.
Another major concern with the development of AI is that AI may soon be smarter than humans, eventually leading to AI taking over humans. Experts have suggested that AI-powered robots will be more intelligent than humans by 2045. Hence, experts and tech enthusiasts are worried that AI-powered robots may take over the human race soon. Also, advanced AI-enabled robots may develop cognitive and behavioral intelligence. With such intelligence, AI may be capable of having feelings and morals and understand what is right and wrong by their own definition, which may not align with human morals. This phenomenon can be especially concerning as there is no globally accepted code of ethics that can be used to design an algorithm for AI.
AI is prone to unintentional bias that can be problematic for certain groups of people. AI bias is generated due to the data that is used to train AI models. If the data used for training has any human bias, then the results generated by AI systems will be biased. Such AI bias may discriminate against people of specific races, genders, or nationalities.
Due to these concerns, experts and tech enthusiasts have trust issues with AI. To build trustworthy AI, developers need to address these concerns and find practical solutions.
Tech companies and developers can consider the following factors for building trustworthy AI:
AI has a serious black box problem, where AI systems make crucial decisions based on machine learning algorithms instead of big data. Hence, end-users and developers may not understand why an AI system made a specific decision. Due to the lack of explanation, users may doubt the accuracy of results generated by AI systems. Hence, developers need to build explainable AI systems. For this purpose, companies that utilize AI have to open the black box and understand how AI systems make crucial decisions and generate results. After clearly understanding how AI systems work, researchers can educate people about AI, making AI systems more transparent. Also, companies that implement AI can also take additional steps to make their AI systems more transparent. For instance, tech giants such as Google and Twitter often release transparency reports that include government requests and surveillance disclosures. Similarly, transparency reports for algorithmic decisions made by AI systems can be helpful in building trust among users.
Machine learning integrity is a necessary condition for developing trustworthy AI systems. Machine learning integrity can help ensure that AI systems generate the output according to a developer’s predefined operational and technical parameters. With machine learning integrity, developers can make sure that AI systems work as they are intended to. Also, developers can set up certain limitations for AI systems that can be used to regulate the usage of AI. In this manner, developers can design trustworthy AI systems that produce accurate results by following predefined conditions.
While developing AI systems, developers need to ensure that the decisions made by AI will benefit humans. For this purpose, AI systems need to be aligned with human principles and values. Hence, the objectives designed for AI systems must align with human values and focus on making human life better. Using this mindset, developers can consciously design applications that will benefit the human race. However, following this approach can be complicated as many developers may build AI applications with good intentions, but these applications invade personal privacy by collecting large volumes of confidential data. In such scenarios, developers can design AI applications that are not too invasive and utilize effective security protocols to safeguard sensitive data. In this manner, developers can design trustworthy AI applications that are secure and highly functional.
Development teams should consist of a diverse range of people who can assist in designing algorithms and collecting a wide variety of training data. Using a wide variety of training data, development teams can ensure that AI systems do not produce biased results. Also, a diverse team will be capable of identifying issues that may go unnoticed with smaller teams, leading to the development of trustworthy AI applications.
Reproducibility ensures that every outcome generated by an AI system can be reproduced. If an outcome is not reproducible, there is no clear way to understand why a result was generated. Also, the outcomes generated by an AI system can be affected by multiple factors such as algorithms, artifacts, system parameters, different versions of code, and various datasets. Hence, ensuring reproducibility can be immensely challenging.
For developing reproducible AI systems, the provenance of every outcome must be maintained. In this manner, developers can understand how each result is generated and identify inaccuracies effortlessly. Hence, to build trustworthy AI systems, developers must focus on generating reproducible outcomes.
The European Union has developed ethics guidelines for building trustworthy AI. These guidelines aim to help developers in building AI systems that are lawful, ethical, and robust. Similarly, government organizations must develop guidelines and regulations for designing trustworthy AI systems. Such regulations can be designed to achieve the following goals:
By considering these points in the development of regulations, governments can guide developers in building trustworthy AI applications.
Similar to debates about trustworthy AI systems, there are other discussions such as paying AI robots and collecting taxes, utilizing AI for developing automated weapons, and granting human rights to AI. These discussions are essential for building a good relationship between AI and humans as they shed light on the promise and perils of the technology. With profound discussions and expert opinions, developers and tech industry giants can understand how to build and implement trustworthy AI applications that can improve human life.