Goto

Collaborating Authors

defendant


Six ways machine learning threatens social justice

#artificialintelligence

When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism. When you use machine learning, you aren't just optimizing models and streamlining business. In essence, the models embody policies that control access to opportunities and resources for many people.


How Your Computer Reinforces Systemic Racism

#artificialintelligence

This summer, my peers marched and spoke out against blatant acts of racial injustice. Meanwhile, as a 17-year-old student who dabbles in computer programming, I've been stewing about a newfangled, less-overt threat that also relates to systemic racism. What I did not realize until this summer was that my generation is already experiencing bias from our most trusted ally: the computer. If you are a student, you may have already been the target of some sort of algorithmic bias, even if you don't know it. Consider one telling fact: for a good number of high schoolers like myself who take state standardized tests, written essays might not be graded not by an English teacher, but by a robot! My first reaction to learning this was simple surprise; I had never thought that my essays might be graded by inanimate objects.


The term 'ethical AI' is finally starting to mean something

#artificialintelligence

Earlier this year, the independent research organisation of which I am the Director, London-based Ada Lovelace Institute, hosted a panel at the world's largest AI conference, CogX, called The Ethics Panel to End All Ethics Panels. The title referenced both a tongue-in-cheek effort at self-promotion, and a very real need to put to bed the seemingly endless offering of panels, think-pieces, and government reports preoccupied with ruminating on the abstract ethical questions posed by AI and new data-driven technologies. We had grown impatient with conceptual debates and high-level principles. And we were not alone. It supersedes the two waves that came before it: the first wave, defined by principles and dominated by philosophers, and the second wave, led by computer scientists and geared towards technical fixes.


Explainable Automated Reasoning in Law using Probabilistic Epistemic Argumentation

arXiv.org Artificial Intelligence

Applying automated reasoning tools for decision support and analysis in law has the potential to make court decisions more transparent and objective. Since there is often uncertainty about the accuracy and relevance of evidence, non-classical reasoning approaches are required. Here, we investigate probabilistic epistemic argumentation as a tool for automated reasoning about legal cases. We introduce a general scheme to model legal cases as probabilistic epistemic argumentation problems, explain how evidence can be modeled and sketch how explanations for legal decisions can be generated automatically. Our framework is easily interpretable, can deal with cyclic structures and imprecise probabilities and guarantees polynomial-time probabilistic reasoning in the worst-case.


New Zealand Has a Radical Idea for Fighting Algorithmic Bias: Transparency

#artificialintelligence

From car insurance quotes to which posts you see on social media, our online lives are guided by invisible, inscrutable algorithms. They help private companies and governments make decisions -- or automate them altogether -- using massive amounts of data. But despite how crucial they are to everyday life, most people don't understand how algorithms use their data to make decisions, which means serious problems can go undetected. The New Zealand government has a plan to address this problem with what officials are calling the world's first algorithm charter: a set of rules and principles for government agencies to follow when implementing algorithms that allow people to peek under the hood. By leading the way with responsible algorithm oversight, New Zealand hopes to set a model for other countries by demonstrating the value of transparency about how algorithms affect daily life.


The term 'ethical AI' is finally starting to mean something

#artificialintelligence

Earlier this year, the independent research organisation of which I am the Director, London-based Ada Lovelace Institute, hosted a panel at the world's largest AI conference, CogX, called The Ethics Panel to End All Ethics Panels. The title referenced both a tongue-in-cheek effort at self-promotion, and a very real need to put to bed the seemingly endless offering of panels, think-pieces, and government reports preoccupied with ruminating on the abstract ethical questions posed by AI and new data-driven technologies. We had grown impatient with conceptual debates and high-level principles. And we were not alone. It supersedes the two waves that came before it: the first wave, defined by principles and dominated by philosophers, and the second wave, led by computer scientists and geared towards technical fixes. Third-wave ethical AI has seen a Dutch Court shut down an algorithmic fraud detection system, students in the UK take to the streets to protest against algorithmically-decided exam results, and US companies voluntarily restrict their sales of facial recognition technology.


Is Artificial Intelligence Artificial Enough? - CSQ

#artificialintelligence

The casual reader may struggle with the concept of biased technology. After all, isn't technology the objective and unemotional translation of zeros and ones into something we can use? How can technology have bias, or any other human emotion? The answer lies in what AI is--and what it isn't. AI, despite its name, is not some miracle technology that replicates the human brain and neural processes to approximate human thought and cognition. AI, at its most basic level, is made up of very complex and very advanced code that enables systems to gather immense amounts of information from different sources, analyze these for patterns and relationships, draw multiple conclusions with different probabilities, and act on these conclusions--all at incredible speed.


Ethical AI- 10 Crimes that Artificial Intelligence May Encourage

#artificialintelligence

Artificial intelligence (AI) may play an increasingly essential role in criminal acts in the future. From a possibility of fraud to deepfakes AI-driven manipulation may cause harm as well. Neural processing engines (NLPs) can help AI take the darker side if they are deployed for all the wrong means. The infamous case of global celebrities caught in the web of deep fakes is not hidden. With non-ethical hackers gaining funds from the dark web and the underworld, the probability of deepfakes only grow larger.


Can AI Replace The Staff In The Judicial System?

#artificialintelligence

In this writing, readers will get to know in what way AI might replace the key procedures in the judicial system around the world. Well, if you wish to discover the role of AI in the judicial system and check a few quite controversial but innovative opinions on the above-mentioned subjects, you should start reading this article immediately! The majority of experts in AI development report that in the future AI will become a decent substitution for human jobs. However, should AI fully replace judges and legal officers? Here, we are going to clarify where AI is implemented in the judicial systems of such high-developed countries as the US and China.


AI bias detection (aka: the fate of our data-driven world)

ZDNet

Here's an astounding statistic: Between 2015 and 2019, global use of artificial intelligence grew by 270%. It's estimated that 85% of Americans are already using AI products daily, whether they now it or not. It's easy to conflate artificial intelligence with superior intelligence, as though machine learning based on massive data sets leads to inherently better decision-making. The problem, of course, is that human choices undergird every aspect of AI, from the curation of data sets to the weighting of variables. Usually there's little or no transparency for the end user, meaning resulting biases are next to impossible to account for.