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The evolution of artificial intelligence is a perpetual stream of wonder, optimism, and terror. AI, like any other technology, may be utilized for good or harm. The same algorithm may be used to enhance healthcare as well as create false news videos. Based on how it is employed, today's AI may be both a huge advantage to society and a threat to democracy.
The tough thing with machine learning is that it may go horribly wrong even if the designers have the best of intentions and want to do well.
Chatbots that become racist, for example, or image recognition algorithms that are trained on non-representative data - none of it was intended to harm anyone, but they did.
Today, corporate trust is at an all-time low. Unintentional repercussions of technology are a danger that might result in public shame and controversies. This can jeopardize people's privacy, health, or lives, as well as harm a company's brand and shareholder profit.
Taking a strong stance has the potential to set one's firm apart from the competitors and increase client satisfaction.
The following are key characteristics and ethics that AI systems must satisfy in order to be considered trustworthy:
Human agency & oversight:
AI systems must empower people by empowering employees to make informed choices and promoting their basic rights. Simultaneously, suitable supervision mechanisms must be in place, which may be accomplished through human-in-the-loop, human-on-the-loop, and human-in-command ways.
Robustness and security in terms of technology:
AI systems must be both robust and secure. They must be safe, with a backup plan in place in case anything happens, as well as accurate, dependable, and repeatable. The only way to assure that inadvertent harm is avoided and prevented is to take proactive measures.
Privacy and data governance:
In addition to providing complete respect for data privacy and security, proper data governance processes must be in place, taking into consideration the data's quality and integrity, and assuring legitimate access to data.
Transparency:
In data, systems, and AI business models are essential. Traceability methods can aid in this endeavor. Furthermore, AI systems & their conclusions should be conveyed in a way that is tailored to the specific stakeholder. Humans must understand that they are communicating with an AI system and be aware of its capabilities and limits.
Diversity, nondiscrimination, and fairness:
Unfair biases must be avoided since they can have a variety of harmful consequences, ranging from marginalization of vulnerable groups to amplification of discrimination and prejudice. In order to promote diversity, Ai technologies should be accessible to everyone, regardless of handicap, and incorporate all key stakeholders throughout their life cycle.
Well-being of society and the environment:
AI systems must benefit all humans, including future generations. As a result, they must be made to be both sustainable and ecologically friendly. They should also include the environment, along with other living creatures, as well as its social and societal implications.
Accountability:
Mechanisms for ensuring accountability and responsibility for AI systems including their outputs must be put in place. Auditability, which allows for the evaluation of algorithms, data, and design processes, is crucial in critical applications. Furthermore, proper and easily accessible reparation should be provided.
Centeredness
Beneficiary
Fair ness
A transparent process for AI decision making
Governing AI
Cooperation
Reliability
Protecting privacy
Security
Bias in algorithms
Diversity in AI teams.
Security-related technical issues should be addressed.
