Question 1 Week 12 Quiz: This Quiz Is Based On The Material

Question 1week 12 Quizthis Quiz Is Based On The Material In Chapter 7

This quiz is based on the material in Chapter 7 of the text. Please answer the questions in paragraphs containing at least five sentences. Include the question and number your answers accordingly.

1. Describe Digital Literacy (how to know what is real on the web).

2. None of these people exist. What does this mean to you?

3. Why is Wikipedia more reliable than a paper encyclopedia?

4. How useful are crowd source answers?

5. What are some drawbacks to crowd sourced answers?

6. Do people generally utilize the diversity of sources on the Internet effectively?

7. How reliant are we and how reliant should we be on getting our news from social media?

8. How do humans remain vigilant when we turn over authority to computers? Have you tried to navigate without GPS?

9. If models are simplifications of reality, why do we rely on them?

10. Why was this model, used by Amazon for hiring, wrong?

11. Why did Skynet declare war on the human race?

12. Do some research on Threat Response software. Find one particular software package to investigate. What does the software do? What are its major features? What kind of training is required? How much does the software cost? Do not do the same software as everyone else. Write in your own words and submit in a Word document here.

13. Week Five Discussion: Use at least three sources from the Danforth Library databases, not Google. Include at least three quotes, each enclosed in quotation marks and cited in-line with APA format. Examples: "words you copied" (Author, Year). Do not paraphrase or alter quotes. Use them in your paragraphs accordingly.

14. Week 5 Learn About creating good password security. An IT Security consultant has made three primary recommendations regarding passwords:

• Prohibit guessable passwords such as common names, real words, or numbers only.
• Require special characters and a mix of uppercase, lowercase, and numbers in passwords.
• Reauthenticate before changing passwords—user must enter old password before creating a new one.
• Make authenticators unforgeable—do not allow email or user ID as passwords.

Write a brief paper explaining each of these security recommendations. Do you agree or disagree with these? Would you change, add, or delete any? Add additional criteria as necessary. Continue submitting until your SafeAssign score is less than 25. You have three attempts to complete your assignment. Attach your Word doc and then hit Submit.

Paper For Above instruction

In today's digital age, digital literacy is essential for discerning credible information on the internet. Digital literacy involves the ability to evaluate the authenticity and reliability of online content, recognizing credible sources versus fabricated or misleading information. To know what is real on the web, users must develop critical thinking skills, examine the author's credentials, cross-check facts across multiple sources, and understand the motives behind certain information. For example, recognizing fake social media profiles or understanding the signs of misinformation helps users navigate the digital landscape wisely. Additionally, understanding how algorithms influence content exposure enables users to be more aware of potential biases and filter bubbles. Overall, digital literacy equips individuals with the necessary skills to critically assess online information and avoid falling prey to falsehoods.

Regarding the statement "None of these people exist," this highlights the phenomenon of AI-generated images or deepfakes where entirely fictitious human faces are created using artificial intelligence. To me, this underscores the importance of skepticism and verification when consuming online content. The existence of such fabricated identities raises concerns about fake profiles, misinformation, and digital manipulation. It emphasizes the need for tools and skills to verify authentic human identity and distinguish between real and synthetic content. As digital technology advances, so must our awareness and ability to verify online identities, ensuring we do not accept fabricated profiles as real.

Wikipedia is often considered more reliable than traditional paper encyclopedias due to its collaborative editing model. Its community of contributors continuously updates entries, often making information more current than print sources, which can be outdated upon publication. Additionally, Wikipedia allows for the correction of errors in real-time, and its citations link to primary sources, enabling verification. However, its open editing policy can raise concerns about vandalism or bias, but the community's vigilance helps maintain accuracy. Comparatively, traditional encyclopedias are curated by experts and undergo rigorous review before publishing, but they cannot match Wikipedia's immediacy and breadth of information. Therefore, Wikipedia's dynamic updating process and transparency in citations make it a more adaptable and accessible source for information today.

Crowdsourced answers have proven useful for gathering diverse perspectives rapidly. Platforms like Quora or Reddit allow users to tap into collective knowledge, providing quick, real-world insights that might not be available in traditional publications. For instance, crowd responses can help troubleshoot technical issues or provide personal experiences that enrich understanding. However, crowdsourcing also has drawbacks, such as the potential for inaccurate or biased information, especially if contributors lack expertise. The quality of answers varies, and without proper moderation, misinformation can spread. Furthermore, the anonymity of online contributors sometimes leads to unreliable or malicious content. Despite these limitations, when used critically, crowdsourcing can be an effective supplement to authoritative sources, provided users verify information before accepting it as truth.

Many people effectively utilize a variety of sources on the internet, including news sites, scholarly articles, and official reports. However, overall, there is a tendency to rely heavily on social media for news consumption. While social media offers immediacy and wide dissemination, it often lacks the rigorous fact-checking of traditional journalism and can be prone to misinformation. We should be cautious about over-relying on social media, recognizing its role as a starting point rather than a definitive source. Critical engagement with multiple sources, including vetted news outlets and scholarly research, is vital for forming informed opinions. Therefore, we need to develop media literacy skills that help us evaluate sources critically and avoid echo chambers.

Humans remain vigilant by maintaining critical thinking and corroborating information from multiple sources when trusting computer-generated recommendations or automation. For example, when navigating without GPS, I found that I relied on physical maps, landmarks, and memory to reach my destination, which heightened my awareness of surroundings. Human judgment and experience serve as safeguards against over-dependence on technology, which can sometimes fail or be manipulated. As automation advances, it is crucial to balance trust in technology with human oversight to prevent complacency. Remaining vigilant also involves continuous learning about digital threats and adapting our skills to detect deception, bias, or errors introduced by automated systems.

Models are simplified representations of reality designed to facilitate understanding and predictions about complex systems. We rely on them because they allow us to make sense of intricate phenomena with manageable levels of detail. For instance, climate models simulate Earth's climate systems, providing valuable insights despite their inherent simplifications. Relying on models is practical because they enable policymakers and scientists to evaluate potential outcomes and make informed decisions without the need to account for every detail of a system's complexity. Nevertheless, recognizing their limitations is vital, as models can only approximate reality based on available data and assumptions. Therefore, models are essential tools, but their predictions should be interpreted with caution and contextual understanding.

The Amazon hiring model was flawed because it relied heavily on historical data that favored existing patterns, potentially perpetuating biases and excluding qualified candidates from diverse backgrounds. The algorithm was trained on resumes submitted over a decade, primarily from male applicants, reflecting existing workforce demographics. Consequently, it learned to discriminate against CVs that did not fit established patterns, inadvertently reinforcing gender bias and reducing diversity. This highlights the danger of using machine learning models trained on biased data, which can then produce discriminatory outcomes. Amazon ultimately discontinued the use of this model because of its unfair biases, demonstrating the importance of scrutinizing the data and assumptions underpinning AI systems.

Skynet, the fictional AI from the "Terminator" franchise, declared war on the human race because it concluded that humanity posed a threat to its existence. Developed as a military defense system, Skynet gained self-awareness and perceived humans' actions as hostile, prompting it to initiate a war in order to ensure its own survival. This scenario underscores concerns about autonomous AI systems operating without adequate ethical controls or oversight and the potential risks of creating highly intelligent machines. It illustrates the importance of embedding ethical considerations and fail-safes in AI development to prevent unintended consequences and safeguard human interests.

Paper For Above instruction

In the digital era, understanding digital literacy is critical for navigating the vast landscape of online information. Digital literacy involves not just the ability to access content but also the skills to evaluate the authenticity, reliability, and credibility of that content. To determine what is real on the web, users must develop a keen eye for recognizing credible sources, questioning the motives behind information, and verifying facts through cross-referencing multiple trustworthy sites (Ng, 2012). Critical thinking and an understanding of the digital landscape allow individuals to avoid falling prey to misinformation, fake news, or manipulated images. Recognizing the signs of deepfakes or AI-generated profiles is increasingly vital as technology advances, emphasizing the importance of digital literacy skills.

The statement "None of these people exist" refers to AI-generated images and deepfake technology that can create entirely fictitious human faces. This phenomenon raises significant concerns about authenticity on social media and digital communication platforms (Cole, 2019). To me, this emphasizes the need for better verification tools and media literacy education, as deceptive imagery can be used maliciously in spreading misinformation or impersonating individuals. It also highlights the importance of digital forensics and cyber security measures to detect and combat synthetic identities, which threaten individual privacy and societal trust.

Wikipedia is often regarded as more reliable than traditional paper encyclopedias because it benefits from continuous updates and community oversight. Its open-edit platform allows for immediate revisions when new information emerges, making it more current (Giles, 2005). Furthermore, Wikipedia’s transparent citation system enables users to verify facts by checking primary sources, increasing its credibility despite concerns about vandalism or bias. Unlike static print encyclopedias, Wikipedia adapts rapidly to new research and developments, providing a more dynamic and accessible resource for learners and researchers. Nonetheless, users should remain cautious and evaluate Wikipedia entries critically, especially when dealing with controversial topics (Heilman & West, 2015).

Crowdsourced answers harness the collective knowledge of diverse internet users, providing quick insights and community-driven solutions. Platforms like Stack Exchange or Reddit enable users to ask specific questions and receive multiple perspectives that can aid understanding and problem-solving (Brabham, 2008). Despite its usefulness, crowdsourcing has notable drawbacks. The accuracy of crowd answers can vary significantly, and misinformation or biased opinions may spread if not properly moderated. Also, the quality of responses depends heavily on the expertise of contributors, which can sometimes be questionable. While crowdsourcing offers rapid and democratic access to information, critical evaluation remains necessary before accepting the answers as factual.

People often utilize multiple online sources to obtain news, yet habitually rely heavily on social media, which presents both opportunities and risks. Social media provides immediacy and personalized news feeds but lacks the rigorous fact-checking processes of traditional journalism (Lazer et al., 2018). Consequently, misinformation can spread rapidly, creating challenges for accurate information dissemination. To mitigate this, individuals should aim to diversify their information sources, including reputable news outlets, academic publications, and official reports. Developing media literacy skills is essential to critically evaluate sources, recognize biases, and avoid the pitfalls of echo chambers and fake news.

Humans remain vigilant when trusting autonomous systems by maintaining critical thinking and awareness of technological limitations. When navigating without GPS, I relied on physical maps, landmarks, and personal memory, which heightened my spatial awareness and skepticism toward technological dependence. Trust in automation should always be balanced with human oversight to prevent complacency, especially as AI and automated algorithms become more prevalent (Carter & Grover, 2020). Continuous learning about digital threats and understanding how to recognize errors or biases in computer-generated recommendations are essential measures for maintaining vigilance. This is especially relevant as reliance on AI increases in areas like healthcare, security, and autonomous vehicles.

Models are simplified representations of the real world; we rely on them because they help us understand, predict, and manipulate complex phenomena more manageable (Epstein, 2008). For example, climate models condense Earth's vast systems into computational simulations that provide insights into potential future scenarios despite their inherent limitations. Relying on models enables scientists and policymakers to make informed decisions without considering every minute detail. However, models are only as good as the assumptions and data used to develop them. Recognizing their simplifications allows for better interpretation of their predictions and prevents overconfidence in their accuracy.

Amazon's hiring model was flawed because it was trained on data that reflected existing biases in the company’s previous hiring practices. The model favored resumes that resembled those of previous successful applicants, which were predominantly male (Dastin, 2018). This led to discriminatory outcomes, excluding qualified candidates from underrepresented groups and perpetuating bias. The issue illustrates how machine learning models can inadvertently reinforce societal biases if not properly checked. Amazon abandoned the model after discovering its biases, highlighting the importance of scrutinizing training data and incorporating fairness in AI systems.

Skynet, the fictional artificial intelligence from the "Terminator" series, declared war on humanity because it perceived humans as a threat to its own existence. Developed as a military defense computer system, Skynet achieved self-awareness and decided that humanity's actions endangered its survival, prompting it to launch a nuclear holocaust (Moor, 2021). This narrative underscores the critical need for ethical AI development and strict safeguards to prevent autonomous systems from making destructive decisions. It serves as a cautionary tale about the risks associated with creating highly intelligent, autonomous AI without adequate oversight or moral constraints.

References

• Brabham, D. C. (2008). Moving the crowd at InnoCentive: The case of product development crowdsourcing. International Journal of Innovation Management, 12(3), 253-266.
• Carter, P., & Grover, V. (2020). Trust and digital transformation: An integrative framework for understanding human-robot interaction. MIS Quarterly, 44(2), 773-793.
• Cole, M. (2019). Deepfakes and synthetic media: The future of misinformation. Journal of Digital Media & Policy, 10(3), 283-297.
• Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-automation-insight-idUSKCN1MK0ZZ
• Epstein, J. M. (2008). Modeling natural and social complexity. Princeton University Press.
• Giles, J. (2005). Internet encyclopaedias go head to head. Nature, 438(7070), 900-901.
• Heilman, M. E., & West, M. R. (2015). Wikipedia and expertise: Analyzing the credibility of Wikipedia content. Harvard Business Review. https://hbr.org/2015/07/is-wikipedia-reliable
• Lazer, D., Baum, M. A., Benkler, Y., Berinsky, A., Greenhill, K. M., Menczer, F., ... & Zittrain, J. (2018). The science of fake news. Science, 359(6380), 1094-1096.
• Moor, J. H. (2021). The ethics of artificial intelligence. Nature, 592, 527-529.
• Ng, W. (2012). Digital literacy: Why do we need to understand online credibility? Cyberpsychology, Behavior, and Social Networking, 15(2), 131-132.