Surveillance practices for data collection and the privacy of court users. The use of AI to perpetuate disinformation is another major ethical problem. Machine learning models can easily generate factually incorrect text, which means that fake news articles or summaries can be created in seconds and distributed through the same channels as real news articles. In the race to adopt rapidly developing technologies, organizations risk overlooking potential ethical implications.
And that could produce undesired results, especially in artificial intelligence (AI) systems that employ machine learning. Machine learning is a subset of AI in which computer systems are taught to learn on their own. Algorithms allow the computer to analyze data to detect patterns and acquire knowledge or skills without having to program them specifically. It is this type of technology that allows voice assistants, such as Apple's Siri or Google Assistant, among many other uses.
In the area of accounting, the many potential applications of AI include real-time auditing and analysis of company finances. Data is the fuel that drives machine learning. But what happens if the data that is supplied to the machine is faulty or the algorithm that guides learning is not properly configured to evaluate the data it receives? Things could go very wrong with astonishing speed. As regulatory and legal frameworks strive to keep up with the rapid pace of technological change, there is growing public demand for greater transparency in how these tools and technologies are used.
The Institute of Business Ethics (IBE) in the United Kingdom recently published a report urging organizations to examine the risks, impacts and side effects that AI could have for their companies and their stakeholders, as well as for society in general. Addressing problems requires these diverse groups to work together. See the 10 questions to ask yourself about adopting or using AI, at the bottom of the page, for the key considerations contained in the IBE report. In the case of machine learning, the greater the complexity of an algorithm, the more difficult it is for users to understand why the machine has made a certain decision.
Another challenge is to avoid bias in the algorithm and in the data set that the algorithm uses to learn. One way to mitigate bias is to use combinations of types of learning, including unsupervised learning, Grosset said. Supervised learning is based on label data, and often labels themselves create biases, he said. Basically, humans bring their own biases to machine learning scenarios.
By contrast, unsupervised learning has no labels and, in essence, you'll find what's in the data without any bias. It's up to the people who create these AI models to ensure that they take ethics into account and to constantly ask themselves how what they create can benefit society as a whole. There are also ways in which for-profit companies can be certified as ethical and sustainable, such as B Corp certification, which validates that an organization uses business as a positive force. But in general, the biggest ethical issues when it comes to artificial intelligence are the biases of AI, the concern that AI could replace human labor, privacy issues, and the use of AI to deceive or manipulate.
It suggests that it is necessary to create a set of guidelines, policies or a code of ethics to ensure that the use of AI at the UN is consistent with its ethical values. Some smaller AI companies have followed suit and are starting to include ethics as part of their core values. In addition to the third objectives, key industry leaders have also developed their own guidelines for using AI in an ethical way. Rather than scaring people or completely ignoring the potential of the unethical use of AI, the first step in the right direction is to ensure that everyone understands the risks and knows how to mitigate them.
When it comes to ethics in AI, the focus is more on the potential use cases and negative impacts of AI, but AI is actually doing a lot of good. Therefore, as AI takes on increasingly sophisticated tasks, it is important to ensure that there is an ethical framework for its proper use. It aims to minimize the risk of ethical errors due to the misuse of AI technologies. An autonomous car that needs to recognize pedestrians, or an algorithm that determines what type of person is most likely to get approved for a loan, can and will have a profound impact on society if ethical guidelines are not implemented.
Google, for example, has developed artificial intelligence principles that form an ethical charter that guides the development and use of artificial intelligence in its research and products. While companies are trying to solve their own ethical issues related to AI, the public sector will also have a role to play. While this type of accreditation is not exclusive to AI companies, it does indicate a commitment to acting ethically, and more and more technology companies can and should apply for certification. In this way, adhering to an ethical AI framework can be seen as creating positive sentiment for your company rather than restrictive regulation.
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