and analysis), completeness( to avoid missing essential patterns and correlations), timeliness, and relevance.
To ensure the AI model is efficient, developers need to collect relevant data, which depends on the choice of sources from which to draw the data. This challenge is compounded by the need to maintain quality and standards to eliminate duplicate or conflicting data. Then, the data must be labeled correctly, a process that can be time-consuming and prone to errors. At the same time, data must be stored to prevent unauthorized access and corruption. Data poisoning is another risk: it refers to a deliberate attack on AI systems, where attackers inject malicious or misleading data into the dataset, resulting in unreliable and even dangerous outputs.
Bias in AI Models
Sometimes, AI can be biased, meaning it might unfairly treat certain groups of people differently. For example, if an AI system is trained on biased data, it may make decisions that discriminate against specific individuals based on factors such as race, gender, or other characteristics.
There are two basic types of bias: explicit and implicit. An explicit bias refers to a conscious and intentional prejudice or belief about a specific group of people. An implicit bias operates unconsciously and can influence decisions without a person realizing it. Social conditioning, the media, and cultural exposure all contribute to these decisions.
Algorithmic bias can creep in because of programming errors, such as a developer unfairly weighting factors in algorithm decision-making based on their own conscious or unconscious biases. For example, indicators like income or vocabulary might be used by the algorithm to discriminate against people of a certain race or gender unintentionally. People can also process information and make judgments based on the data they initially selected( cognitive bias), favoring datasets based on Americans rather than a sampling of populations worldwide.
Bias in AI is not merely a technical issue but a societal challenge, as AI systems are increasingly integrated into decisionmaking processes in healthcare, hiring, law enforcement, the media, and other critical areas. Bias can occur in various stages of the AI pipeline, especially with data collection. Outputs may be biased if the data used to train an AI algorithm is not diverse or representative of the actual data. For instance, training that favors male and white applicants may result in biased AI hiring recommendations.
Labeling training data can also introduce bias since it can influence the interpretation given to the outputs. The model itself might be imbalanced or fail to consider diverse inputs, favoring majority views over those of minorities. To make AI more accurate and fairer, researchers need to retrain it regularly. Companies, especially insurers, must ensure that they use accurate, complete, and up-to-date data while also ensuring their models are fair to everyone.
Transparency
Transparency is a key issue, as it can be challenging to explain how AI makes its decisions. This lack of clarity can be a problem for both customers and regulators who want to understand how these systems work. Transparency in AI is essential because it provides a clear explanation for why AI’ s decisions and actions occur, allowing us to ensure they are fair and reliable.
Using AI in the workplace can help with the hiring process, but understanding how AI does so without bias can only be achieved if it is transparent. As AI becomes increasingly important in society, business, healthcare, the media, and culture, governments and regulators need to establish rules, standards, and laws that ensure transparency in the use of AI.
Transparency is closely related to Explainable AI( XAI), which allows outsiders to understand why AI is making its decisions. Such explainability builds customer trust. This is referred to as a glass box system, as opposed to a black box system, where the results or outputs from AI are transparent and the reasons for their decisions are known, sometimes even to the system’ s developer.
Errors in AI Predictions
AI can sometimes produce incorrect results, known as false positives or false negatives. This happens because the data used to train AI systems is often imperfect, leaving room for errors. It’ s human nature to overestimate a technology’ s short-term effect and underestimate its long-term effect. This tendency certainly applies to AI predictions. The question, of course, is how long the long run is.
The rise of generative AI confronts us with key questions about AI failure and how we make sense of it. As most experts( and many users) acknowledge, AI outputs, as astonishing and incredibly powerful as they can be, may also be fallible, inaccurate, and, at times, completely nonsensical. A term has gained popularity in recognition of this fallibility—“ AI hallucination.”
The scholar and bestselling author Naomi Klein argued in an article for the Guardian in May 2023 that the term“ hallucination” only anthropomorphized a technical problem and that,“ by appropriating a word commonly used in psychology, psychedelics and various forms of mysticism, the tech-industry is feeding the myth that by building these large language models, we are in the process of birthing an animate intelligence.”
Nonetheless, all major AI developers, including Google, Microsoft, and OpenAI, have publicly addressed this issue, whether it is called a hallucination or not. For instance, an internal Microsoft document stated that“ these systems are built to be persuasive, not truthful,” allowing that“ outputs can look very realistic but include statements that aren’ t true.” Alphabet, the parent company of Google, has admitted that it’ s a problem“ no one in the field seems to have solved.” That means AI outputs cannot be entirely relied upon for their predictions and need to be verified by reliable sources.
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