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Related Concept Videos

Confirmation Biases01:31

Confirmation Biases

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Hindsight Biases01:12

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Diode: Forward bias01:20

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In semiconductor devices, diodes play a crucial role in directing current flow, and its operation is primarily categorized into forward bias and reverse bias. A diode is said to be forward-biased when its p-type region is connected to the positive terminal of a battery and its n-type region is linked to the negative terminal. This configuration reduces the potential barrier within the diode, allowing current to flow easily from the p to the n-type region.
The behavior of a diode in forward bias...
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Biasing of FET01:22

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Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
In an N-channel JFET, the structure consists of N-type material forming the channel on a P-type substrate, with the...
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Biasing of P-N Junction01:16

Biasing of P-N Junction

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The operation of a p-n junction diode involves various biasing conditions, including forward bias, reverse bias, and equilibrium.
In equilibrium, no external voltage is applied across the p-n junction. The depletion region is formed at the junction interface due to the diffusion of carriers, which leaves behind charged dopants, acceptors on the p-side, and donors on the n-side. These immobile charges create an electric field that prevents further diffusion of carriers. The related energy band...
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Engineering Bias in AI.

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    This summary is machine-generated.

    Societal bias significantly impacts artificial intelligence (AI) development. Addressing limited diversity is crucial to mitigate AI risks and ethical concerns.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence (AI)
    • AI Ethics

    Background:

    • Timnit Gebru's background includes circuit design at Apple and a Ph.D. in computer vision from Stanford.
    • Postdoctoral work at Microsoft Research focused on Fairness, Transparency, Accountability, and Ethics in AI (FATE).
    • Co-founder of Black in AI and currently a research scientist at Google's Ethical AI team.

    Purpose of the Study:

    • To explore the influence of societal bias in the engineering of AI systems.
    • To discuss the deficits and dangers arising from a lack of diversity in the AI field.
    • To examine the inherent challenges in addressing complex ethical issues within AI.

    Main Methods:

    • Interview-based discussion with Timnit Gebru.
    • Exploration of research on algorithmic bias and data mining ethics.
    • Analysis of societal factors influencing AI development.

    Main Results:

    • Societal biases are deeply embedded in AI engineering processes.
    • Limited diversity in AI exacerbates ethical risks and technical deficits.
    • Addressing these issues requires a multifaceted approach to AI ethics and development.

    Conclusions:

    • The integration of societal bias into AI necessitates critical examination.
    • Promoting diversity is essential for safer and more equitable AI.
    • Ethical AI development faces significant, complex challenges requiring ongoing attention.