British astronaut, Major Tim Peake, takes data to new dimensions as he closes out Big Data LDN 2023
are available only for registered users
Continue Reading By Registering For Free
The launch of OpenAI’s explosive chatbot ChatGPT has spurred a whole new era of AI innovation that continues to define the enterprise landscape. However the sudden rise of AI comes with a host of new ethical issues that need to be addressed before the technology transforms society as we know it.
Experts at Google Deepmind have already developed their own ethical principles to guide the tech giant’s development of AI and ensure the ethical advancement of the technology. Non-profits like the AI Now Institute, have also begun weighing in on how we can ethically control AI to protect society from the risks that come with it. Governments are taking note too. The EU is in the final stages of introducing the world’s first AI safety act, marking a new era of regulation for ethical AI development...
AI systems learn to make decisions based on training data, which includes biased human decisions or historical or social inequities – even if societal factors such as gender, race, or orientation are removed. An infamous example of this bias was recently uncovered in a clinical algorithm that hospitals across the US were using to identify patients who would benefit from extra medical care. A bombshell study found the algorithm was assigning unfairly low-risk scores to black patients, using patients’ past healthcare costs as a way to gauge their medical needs – which ultimately functioned as a proxy for race.
The problem with AI bias is that it’s difficult to know about it until it’s already programmed into the software. This makes it difficult to prevent, requiring an approach that not only includes rigorous data preprocessing to identify and mitigate bias but also involves diverse development teams and the establishment and adherence to ethical AI frameworks to guide responsible development and usage. Regulatory bodies must also play a pivotal role in setting clear guidelines and penalties for non-compliance, ensuring that AI systems are designed and operated with fairness, transparency, and accountability at their core.
AI relies on vast amounts of data to train its algorithms and improve its performance. While most of this data is publicly available, it can also include sensitive information such as names, addresses and even financial information that are inadvertently scraped from the internet during the training process. This leads to the possibility of AI accidentally generating content containing sensitive information that exposes people’s personal information and goes against privacy regulations around the world.
AI-powered surveillance systems and data mining techniques can also pose a significant threat to people’s privacy in public. Facial recognition technologies, for instance, have been used by law enforcement agencies to identify people and monitor their activities in public and private spaces. This data can be used to create detailed profiles of individuals, which could be exploited for a variety of purposes, including targeted advertising, social engineering, and even political repression.
With AI systems growing increasingly complex and influential, the need for transparency in their decision-making processes is more important than ever before. Many AI algorithms operate as "black boxes," meaning that even their creators may not fully comprehend how they arrive at specific decisions. This raises concerns about transparency in their decision-making, as it becomes challenging to trace and explain the reasoning behind AI-generated outcomes, especially in high-stakes applications like healthcare and autonomous vehicles.
Transparency is crucial to identifying and rectifying biases within AI systems and ensuring that they do not unfairly discriminate against certain groups. It also enables accountability by allowing stakeholders to understand how decisions are made and hold developers responsible when things go wrong.
Continue Reading By Registering For Free
Revelate: Finding Success with Data Products
Pivotree and Syndigo: Unlocking Value Through Content Strategy
Unlike OpenAI’s ChatGPT, Pryon is Purpose-Built for the Enterprise
Case study: Fortune 500 Manufacturer Triples Accuracy in IT Support Answers with Pryon
Pryon: The State of AI for the Enterprise
Prophecy: How Low-Code Data Engineering can 10x Productivity on the Data Lakehouse
Nile: IDC’s Key Criteria When Selecting a NaaS Solution
CyberMaxx: Monitoring Cyber Risk and How Offence Fuels Defence
Want a Good Job? UK Students Say You Need a Degree
Salvador Technologies: The Art of Cyber Attack Recovery