Ethical AI is a set of principles for creating artificial moral agents. This concept encompasses various concepts from ethical philosophy.
AI raises many security and privacy issues. This is particularly the case when considering facial recognition software or any other personal data collection tools.
Creating a Code of Ethics
As AI becomes an integral part of society, organizations and individuals developing and using these technologies have a responsibility to use them ethically and make sure that all systems available to all are used justly – making sure their use does not negatively affect anyone. This may require regular monitoring to ensure no negative consequences arise from using these systems.
An ethical code can assist these efforts, and there are various codes available. Some focus specifically on AI while others cover more general matters. When creating these codes it should be done so in consultation with stakeholders including employees and customers to ensure it reflects their values accurately.
Notable points regarding AI ethical considerations also vary across industries and types of organizations; government agencies typically put safety and lawfulness first while private-sector firms tend to prioritize fairness over safety. Furthermore, cultural factors can have an enormous influence on how principles such as these are applied; therefore it’s essential that AI solutions developed take account of local culture and values when designing them.
There are various resources for those wishing to increase their understanding of AI ethics, including online courses and books. Furthermore, institutions specializing in ethics and AI research – like Oxford – also provide excellent resources. Combined together these initiatives form a powerhouse of research and knowledge on this topic that transcends its individual parts.
Creating a Diverse and Inclusive Data Set
AI’s potential to amplify biases regarding race, gender, political leanings and other variables can have devastating repercussions. While there are technical measures in place to detect and remove such biases, their solutions have their limits; without changing the underlying code completely it may still be difficult to pinpoint why certain people were treated differently and what caused this behavior.
One such issue occurred with Apple Card’s algorithm when allegations surfaced alleging it to provide men with higher credit limits than women, even when their scores and other factors were equivalent. Although this case was rare, AI developers need to be mindful that their systems could create hidden biases that are hard to detect or fix.
Biases in training data sets or algorithms themselves, or through how problems are presented or addressed in training data sets can also introduce bias. Furthermore, other forms of biases may exist depending on how problems are framed.
Ethics are among the greatest challenges AI faces and can have far-reaching repercussions for society as a whole. A strong ethical framework can help mitigate risks while those working with the technology must also recognize and address potential ethical concerns quickly before they can escalate into serious issues.
Creating a Fairness Threshold
As AI usage becomes more widespread, ethical concerns will increase accordingly. Companies like Google and Microsoft have taken proactive measures to avoid biased algorithms from being integrated into their products. They’ve even created ethics charters and business schools dedicated to teaching other businesses how to implement ethical AI into their algorithms.
One of the main goals is to establish a fairness threshold to ensure algorithms are not biased, using disparate impact analysis or individual tests such as seeing whether tall people default on AI-approved loans more frequently. There are various definitions of fairness; most importantly it ensures no protected group is targeted unfairly by an algorithm. This may be measured through disparate impact, measuring how often individuals in one group are selected or excluded from processes or tested individually such as whether tall people default more often on loans approved by AI than short people.
One way to combat biased algorithms is ensuring the teams working on them are diverse. Software programmer demographics tend to skew towards male and white, so taking steps to include more women and minorities will ensure AI algorithms reflect and understand their environment better. Transparent decision making processes also help detect any hidden biases; that is why explainable AI models provide businesses with a chance to spot any potential issue quickly and address it swiftly.
Creating a Testing Process
Laws and regulations play an essential role in upholding ethical AI; however, companies using and developing this technology as well as those providing it to their users have an equal obligation to set ethical standards themselves. This involves creating an ongoing testing/reviewing process for their systems before deployment, during deployment and on an ongoing basis to ensure they adhere to expected ethical norms.
As part of this process, the first step should be creating a code of ethics which clearly outlines the values and principles that guide your company and AI systems. Furthermore, an internal governance mechanism for assessing and managing ethical AI, such as an AI Ethics Board is also recommended – providing centralized review, decision-making, and oversight processes of your ethical AI policies and practices.
Make sure the data used to train your AI system is diverse and inclusive to avoid perpetuating biases and unfair outcomes. Georgia Tech researchers who studied object recognition for self-driving cars discovered that models hit pedestrians with dark skin 5% more often than those with light skin due to training data that included fewer dark-skinned people than lighter-skinned individuals.
