# A Systematic Review of Responsible Artificial Intelligence Principles and Practice
## 6 Finding 4: The Need for Responsible AI  
The need for responsible AI is driven primarily by the severity of challenges, risks, and harms posed by AI systems [26]. The physical harms of malfunctioning system operations due to excess AI automation, the psychological harms due to decision-making by biased or erroneous AI systems, the lack of human-in-the-loop processes for managing and supervising AI systems, hallucinations and inaccuracies as well as social issues of technological job displacement, AI-driven misinformation, and deepfakes are some of the widely recognized and publicly experienced risks of AI. As the reliance on black box machine learning models increases for making sensitive predictions, stakeholders within the AI community are increasingly advocating for greater transparency to understand the inner workings of these algorithms [26]. The demand for transparency is driven by the need to ensure ethical AI practices and to address concerns related to biases, fairness, and potential discrimination. The reluctance to adopt techniques that lack direct interpretability, tractability, and trustworthiness stems from the recognition of the importance of responsible AI implementation [26]. Stakeholders, including organizations, policymakers, and the public, increasingly emphasize the need for AI systems to be understandable and explainable. Interpretable AI systems are seen as important for public acceptance, building trust, and enabling effective oversight and regulation. Identifying the implicit assumptions of AI algorithms poses a significant challenge [40; 66]. Implicit biases and contextual influences can impact the accuracy and reliability of AI systems, making it essential to unravel and comprehend the underlying assumptions to address potential limitations and biases. The opacity of AI results further complicates decision-making processes that rely on deliberative rationality [67]. When AI results are difficult to understand and interpret, it becomes challenging for human decision makers to assess the validity, reliability, and potential biases associated with the outcomes. This opacity can hinder effective decision-making, especially in contexts where human judgment and reasoning are crucial. The development and training processes of AI systems rely heavily on human inputs, which introduces the potential for inherent biases of programmers to manifest within algorithms [13]. This raises concerns about the replication and perpetuation of biases present in training data, resulting in biased results and decision-making. The presence of biased AI systems poses significant ethical challenges as it has the capacity to reinforce and perpetuate existing societal inequalities and discrimination. In the current context of AI research, privacy issues arising from AI-powered technologies have become a significant concern. One of the prominent risks is the potential compromise of privacy due to sensitive, granular, and in-depth data collection practices [24]. As AI systems collect vast amounts of personal information, there is an increased risk of unauthorized access, data breaches, and misuse of personal data, which can lead to privacy violations. Moreover, the use of customer data for marketing purposes poses privacy challenges [42]. AI-powered systems can analyze customer data to generate hyper-personalized targeting strategies, raising questions about the transparency and consent surrounding data usage. The extensive profiling and targeting capabilities of AI algorithms can result in privacy invasion and the potential manipulation of individuals' preferences and behaviors. AI errors can cause risks and harms to individuals, businesses, and society as a whole. One of the key factors contributing to AI errors is when an algorithm is tasked with making predictions beyond the scope of its training data [40]. AI models are trained on specific datasets, and their accuracy and reliability are optimized for the tasks for which they were originally designed.