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<\/p>\nArtificial intelligence (AI) is advancing at a breakneck pace, and it is swiftly emerging as a potential disruptor and vital enabler for almost every business in every sector. At this point, the technology itself is not a barrier to the mainstream use of AI; rather, it a collection of challenges that, unfortunately, are far more human, such as ethics, governance, and human values, are.<\/p>\n
The challenges that come with the increased use of AI are expected to grow, with potentially major repercussions for society as a whole and even worse repercussions for the organizations using it. Organizations must develop effective methods for identifying and managing AI-related issues. Organizations need to formulate an AI strategy that is accepted and spoken from the mailroom to the boardroom to realize the promise of human and machine partnership.<\/p>\n
Need to build a trustworthy AI framework<\/strong><\/p>\n By establishing an ethical framework, organizations must provide all of their internal and external stakeholders with a uniform language to express trust and assure data integrity.<\/p>\n At every level of the AI lifecycle, the trustworthy AI framework is intended to help an organization in identifying and minimizing possible risks connected to AI. Here\u2019s a close look at 5 fundamentals to consider while building trustworthy AI:<\/p>\n 1. Technical robustness and safety<\/strong><\/p>\n Technical robustness, which is closely related to the idea of preventing harm, is an essential element in achieving trustworthy AI. Technical robustness necessitates the development of AI systems with a risk-prevention mindset, in a way that ensures they consistently act as intended while minimizing unintended and unanticipated harm and preventing intolerable harm.<\/p>\n AI systems like any other software systems, need to be secured against flaws that might be used for malicious purposes, like hacking. Attacks may target the model (model leaking), the data (data poisoning), or the underpinning infrastructure (software and hardware). For example, when an AI system is attacked, such as in adversarial attacks, both the data and the behavior of the system may be altered, causing the system to behave differently or even to shut down.<\/p>\n 2. Governance and data privacy<\/strong><\/p>\n A fundamental right that is notably impacted by AI systems is privacy. Adequate data governance must address the quality and integrity of the data used, its relevance is given the domain in which the AI systems will be used, its access protocols, and all of these issues.<\/p>\n Throughout a system\u2019s whole lifespan, AI systems must provide privacy and data protection. This comprises the data the user initially submitted as well as the data created about the user throughout their contact with the system. For example, individuals must be given confidence in the data collection process by having assurances that the information gathered about them won\u2019t be utilized unjustly or unfairly to discriminate against them.<\/p>\n 3. Diversity and fairness<\/strong><\/p>\n Organizations must promote inclusivity and diversity across the whole life cycle of an AI system if they want to develop trustworthy AI. This involves not only taking into account and including all involved parties in the process, but also guaranteeing equitable access and treatment through inclusive design procedures. The idea of fairness is directly related to this need.<\/p>\n Inadvertent historic bias, incompleteness, and poor governance models may be present in data sets utilized by AI systems (both for training and operation). The persistence of such biases may result in unintentional (in)direct prejudice and discrimination against particular groups or individuals, thus escalating prejudice and marginalization.<\/p>\n Unfair biases may also affect how AI systems are created, such as when developing algorithms. This might be avoided by putting in place supervision procedures that examine and deal with the system\u2019s goals, limitations, needs, and choices clearly and openly. For example, it is advisable to promote recruiting people from a variety of specialties, backgrounds, and cultures to guarantee a diversity of viewpoints.<\/p>\n 4. Accountability<\/strong><\/p>\n Along with the aforementioned standards, accountability has a tight relationship to the fairness concept. In order to assure responsibility and accountability for AI systems and their results, both before and after their creation, deployment, and usage, processes must be put in place.<\/p>\n Enabling the evaluation of algorithms, data, and design processes is a need for accountability. This doesn\u2019t imply that details of commercial plans and intellectual property about the AI system must constantly be made public. For example, the examination of the technology by internal and external auditors, as well as the availability of such evaluation reports, may help establish its reliability.<\/p>\n
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