Human-computer interaction
Human-computer interaction

Human-computer interaction

Algorithms Artificial Intelligence Computer science and technology Data Ethics Human-computer interaction IDSS Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Research School of Engineering Video

Study: AI could lead to inconsistent outcomes in home surveillance

Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.

Artificial Intelligence Computer science and technology Data Human-computer interaction Law machine learning Media Lab Privacy Research School of Architecture and Planning Technology and society

Study: Transparency is often lacking in datasets used to train large language models

Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.

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First AI + Education Summit is an international push for “AI fluency”

The three-day, hands-on conference hosted by the MIT RAISE Initiative welcomed youths and adults from nearly 30 countries.

Artificial Intelligence Computer science and technology Data Electrical Engineering & Computer Science (eecs) Human-computer interaction IDSS Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Research School of Engineering Wind

MIT researchers use large language models to flag problems in complex systems

The approach can detect anomalies in data recorded over time, without the need for any training.

Algorithms Artificial Intelligence Data Electrical Engineering & Computer Science (eecs) Human-computer interaction machine learning MIT Schwarzman College of Computing MIT-IBM Watson AI Lab Research Research Laboratory of Electronics School of Engineering

Method prevents an AI model from being overconfident about wrong answers

More efficient than other approaches, the “Thermometer” technique could help someone know when they should trust a large language model.

Algorithms Artificial Intelligence Computer science and technology Data Electrical Engineering & Computer Science (eecs) Human-computer interaction Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Research School of Engineering

Study: When allocating scarce resources with AI, randomization can improve fairness

Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.

Algorithms Artificial Intelligence Computer science and technology Economics Human-computer interaction Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Research School of Engineering School of Humanities Arts and Social Sciences

Large language models don’t behave like people, even though we may expect them to

A new study shows someone’s beliefs about an LLM play a significant role in the model’s performance and are important for how it is deployed.

Algorithms Artificial Intelligence Computer science and technology Data Human-computer interaction IDSS Laboratory for Information and Decision Systems (LIDS) machine learning Mechanical engineering MIT Schwarzman College of Computing Research School of Engineering

How to assess a general-purpose AI model’s reliability before it’s deployed

A new technique enables users to compare several large models and choose the one that works best for their task.

Artificial Intelligence Computer science and technology Data Electrical Engineering & Computer Science (eecs) Human-computer interaction Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Research School of Engineering Technology and society

When to trust an AI model

More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.

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“They can see themselves shaping the world they live in”

Developed by MIT RAISE, the Day of AI curriculum empowers K-12 students to collaborate on local and global challenges using AI.