Managing Ethical Challenges in an AI Environment
In this article, we talk about the ethical challenges in AI, and how we at Repustate, manage these issues while developing our products and solutions.
AI technology is being implemented virtually by every industry in some form or the other, which along with business innovations has also lead to AI ethical issues popping up. In this article, we talk about the ethical challenges in AI, and how we at Repustate, manage these issues while developing our products and solutions.
What Are The Major AI Ethical Issues?
There are several major AI ethical issues. In everyday business, there is the issue of AI-based social media designed marketing that uses data to track people’s information and use it for advertising without consent. Then there is the lack of accountability in certain areas of defense and surveillance which are shaded gray. Another challenge that AI poses is that even though it has augmented efficiency and productivity, it has lead to a growing chasm between giant enterprises like Amazon and small & medium businesses. Let’s discuss these and other challenges in detail.
Data privacy is a very crucial factor in AI algorithms and plays a large part in AI ethical issues. This is mostly because of the immense amount of data it takes to train a sentiment analysis engine. Depending on the use of the AI, there can be serious implications if this data is misused or not protected. Healthcare & pharma, banking, schools and educational institutions, are the first that come to mind from the point of view of the public, apart from defense, national trade, and other such sensitive areas. Companies like Facebook have come under the spotlight in recent years as governments and the public realize how important it is to regulate companies who seem to have a hard time balancing profit and ethics.
Human bias in the very development of an AI algorithm is the crux of the issue of bias, which is fast becoming one of the biggest ethical challenges in AI. Take for example, how the AI algorithms used in facial recognition systems have a hard time identifying dark complexioned people compared to the fair-skinned public, especially in the United States. This problem is worse in the case of Black women. An African American data scientist experienced this bias personally, when during her research at Georgia Institute of Technology, the AI programs she was using worked well on her Caucasian classmates and friends but failed to recognize her.
This problem can be solved only if the corpus that the AI or machine learning algorithm is being trained is vast, and more importantly, diverse. Solving this problem is of utmost importance as it has lead to many false accusations, and worse, false identification in criminal matters that have lead to false imprisonments.
AI and machine learning are still expensive technologies. Whether you need a chatbot or an AI machine for medical imaging, the cost of an AI product has a wide range that depends on several factors. The high cost is usually because of the amount of data you use to train a model or the depth of customization required. The level of intelligence and complexity of the functionality is also a big factor, and so is the fact that the technology is not as prevalent as it should be. This had lead to a widening divide between large enterprises with huge resources and small & medium sized businesses that find the technology inaccessible due to monetary constraints. This can result in long-lasting AI ethical issues due to AI-powered economies.
AI is used for CGI and to create deep fakes in the entertainment business but when this same technology is used for false marketing, it is a serious concern. Additionally, deep fakes can have copyright issues and can breach data protection laws as well. Similarly, AI bots are used to create misleading information on social media for provocative propaganda, whether for political gains or financial frauds. Ironically, we are using AI itself to combat this menace.
How Can We Manage Ethical Challenges in AI
As all things artificial go, ultimately these are man-made issues, and can be addressed through sound business regulations, resourcefulness, and adherence to privacy and security laws. At Repustate, we take ethical challenges in AI very seriously and are cognizant of them while developing our sentiment analysis platforms and working with clients all over the world.
AI ethical issues somehow or the other always revolve around data privacy because at the end of the day you are either using data for training a model or processing data using an API. At Repustate, even our own data scientists have no access to a client’s data unless given express permission. As our sentiment analysis API processes millions of API calls a day, it accesses data which can be very sensitive. Our clients in healthcare, brand reputation management, quantitative trading, government, and numerous other sectors trust us with their sensitive data, and we in return make sure that there is zero chance of a data breach. It is for this reason that our platforms are available as an on-premise installation behind your firewall, as well as a cloud API.
As we read earlier, the bias that an AI machine has, is almost always because of the quality of the data that is used to train it. We offer a truly multilingual video content analysis and social media listening platform for emotion mining that can cater to businesses with a diverse customer base, with high efficiency. Our models never use translations because every single one of the 23 languages our platforms are available in has a unique part of speech tagger, developed precisely for that language.
Research shows that businesses can grow at a significant pace if they cater their content to their customers in the language they speak. That’s why we support our clients in their endeavour to reach wider markets by providing customizable, machine learning models that offer several NLP and ML tasks including search inside video, while analyzing data in the native tongue, without translations. This ensures accurate customer and employee insights, whether you are a healthcare provider or a retailer.
This is our way of ensuring that ethical challenges in AI like these are not overlooked in our sentiment models. Our sentiment analysis dashboard itself is in the native language, which means it’s not just the data that you want to analyze that is read in the native language but also the insights that you receive.
AI ethical issues also pertain to how easily it is accessible to businesses. Every organization, however modest, should be able to use machine learning to enhance their productivity and, as a result, their ROI. Repustate has always believed in making high accuracy and agile machine learning models available to all customers, regardless of their size. Whether you are a small and medium size (SMB) company that wants to analyze a new target market, or a government organization that wants to completely overhaul the country’s education delivery method, Repustate ensures that you get the best AI-powered sentiment analysis and text analytics solution whatever the scale of your project.