A University of Kansas interdisciplinary team led by relationship psychologist Omri Gillath has published a new paper in the journal Computers in Human Behavior showing people’s trust in artificial intelligence (AI) is tied to their relationship or attachment style.
The research indicates for the first time that people who are anxious about their relationships with humans tend to have less trust in AI as well. Importantly, the research also suggests trust in artificial intelligence can be increased by reminding people of their secure relationships with other humans. Read More
Tag Archives: Trust
When governments turn to AI: Algorithms, trade-offs, and trust
Artificial intelligence can help government agencies solve complex public-sector problems. For those that are new at it, here are five factors that can affect the benefits and risks.
As artificial intelligence (AI) and machine learning gain momentum, an increasing number of government agencies are considering or starting to use them to improve decision making. Additionally, COVID-19 has suddenly put an emphasis on speed. In these uncharted waters, where the tides continue to shift, it’s not surprising that analytics, widely recognized for its problem-solving and predictive prowess, has become an essential navigational tool. Some examples of compelling applications include those that identify tax-evasion patterns, sort through infrastructure data to target bridge inspections, or sift through health and social-service data to prioritize cases for child welfare and support, or predicting the spread of infectious diseases. They enable governments to perform more efficiently, both improving outcomes and keeping costs down. Read More
There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks
Artificial Intelligence (AI) plays a fundamental role in the modern world, especially when used as an autonomous decision maker. One common concern nowadays is “how trustworthy the AIs are.” Human operators follow a strict educational curriculum and performance assessment that could be exploited to quantify how much we entrust them. To quantify the trust of AI decision makers, we must go beyond task accuracy especially when facing limited, incomplete, misleading, controversial or noisy datasets. Toward addressing these challenges, we describe DeepTrust, a Subjective Logic (SL) inspired framework that constructs a probabilistic logic description of an AI algorithm and takes into account the trustworthiness of both dataset and inner algorithmic workings. DeepTrust identifies proper multi-layered neural network (NN) topologies that have high projected trust probabilities, even when trained with untrusted data. We show that uncertain opinion of data is not always malicious while evaluating NN’s opinion and trustworthiness, whereas the disbelief opinion hurts trust the most. Also trust probability does not necessarily correlate with accuracy. DeepTrust also provides a projected trust probability of NN’s prediction, which is useful when the NN generates an over-confident output under problematic datasets. These findings open new analytical avenues for designing and improving the NN topology by optimizing opinion and trustworthiness, along with accuracy, in a multi-objective optimization formulation, subject to space and time constraints. Read More
Introducing the Model Card Toolkit for Easier Model Transparency Reporting
Machine learning (ML) model transparency is important across a wide variety of domains that impact peoples’ lives, from healthcare to personal finance to employment. The information needed by downstream users will vary, as will the details that developers need in order to decide whether or not a model is appropriate for their use case. This desire for transparency led us to develop a new tool for model transparency, Model Cards, which provide a structured framework for reporting on ML model provenance, usage, and ethics-informed evaluation and give a detailed overview of a model’s suggested uses and limitations that can benefit developers, regulators, and downstream users alike.
Over the past year, we’ve launched Model Cards publicly and worked to create Model Cards for open-source models released by teams across Google. Read More
Ernst & Young’s (EY) bridging AI’s trust gaps
The rapid development of artificial intelligence (AI) is raising urgent questions about ethical and consumer protection issues — from potential bias in algorithmic recruiting decisions to the privacy implications of health monitoring applications.
This survey finds that policymakers have a clear vision of AI ethical risks — and are moving to implementation, while, in contrast, a much weaker consensus exists among companies. Read More
This Technique Uses AI to Fool Other AIs
Artificial intelligence has made big strides recently in understanding language, but it can still suffer from an alarming, and potentially dangerous, kind of algorithmic myopia.
Research shows how AI programs that parse and analyze text can be confused and deceived by carefully crafted phrases. A sentence that seems straightforward to you or me may have a strange ability to deceive an AI algorithm. Read More
The Four Components of Trusted Artificial Intelligence
Trust and transparency are at the forefront of conversations related to artificial intelligence(AI) these days. While we intuitively understand the idea of trusting AI agents, we are still trying to figure out the specific mechanics to translate trust and transparency into programmatic constructs. After all, what does trust means in the context of an AI system? Read More
DeepCode taps AI for code reviews
By leveraging artificial intelligence to help clean up code, DeepCode aims to become to programming what writing assistant Grammarly is to written communications.
Likened to a spell checker for developers, DeepCode’s cloud service reviews code and provides alerts about critical vulnerabilities, with the intent of stopping security bugs from making it into production. The goal is to enable safer, cleaner code and deliver it faster. Read More
Trust, control and personalization through human-centric AI
Our virtual lives lie in the hands of algorithms that govern what we see and don’t see, how we perceive the world and which life choices we make. Artificial intelligence decides which movies are of interest to you, how your social media feeds should look like, and which advertisements have the highest likelihood of convincing you. These algorithms are either controlled by corporations or by governments, each of which tend to have goals that differ from the individual’s objectives.
In this article, we dive into the world of human-centric AI, leading to a new era where the individual not only controls the data, but also steers the algorithms to ensure fairness, privacy and trust. Breaking free from filter bubbles and detrimental echo chambers that skew the individual’s worldview allows the user to truly benefit from today’s AI revolution.
While the devil is in the implementation and many open questions still remain, the main purpose of this think piece is to spark a discussion and lay out a vision of how AI can be employed in a human-centric way. Read More
The devil you know: trust in military applications of Artificial Intelligence
This article was submitted in response to the call for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It is based on a chapter by the authors in the forthcoming book ‘AI at War’ and addresses the fifth question (part d.) which asks what measures the government should take to ensure AI systems for national security are trusted — by the public, end users, strategic decision-makers, and/or allies. Read More
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