---
owner:
    hid: 104
    name: Jones, Gabriel
    url: https://github.com/bigdata-i523/hid104
paper1:
    abstract: >
        We breifly analyze the history of data to show how having Lots
        of Data hardly differs from data storage and analysis in the
        early days of SQL, or even before computers. We then explain
        how Big Data represents a paradigmatic shift from conventional
        data analysis. We then begin to look at the potential limits
        of Big Data to assert that this paradigmatic shift does not
        mean the end of science. We conclude that misunderstanding Big
        Data prevents organizations from capitalizing on its potential
        and can lead them to spurious answers.
    author:
        - Jones, Gabriel
    chapter: Theory
    hid:
        - 104
    status: Oct 28 17 100%
    title: What Separates Big Data from Lots of Data?
    url: https://github.com/bigdata-i523/hid104/tree/master/paper1
paper2:
    review: Nov 10 2017
    abstract: >
        Since its origins, Big Data has promised an unimaginable
        potential to revolutionize the world. Scholars have wisely
        noted that it represents a paradigmatic shift from
        conventional norms of data, but the public has quickly latched
        onto provocative but unrealistic narratives that deify big
        data as omniscient, infallible, and devoid of bias. Confiding
        in such narratives diminishes the integrity of credible
        science and poses serious ethical challenges, but these
        challenges are more likely overlooked because the very
        narratives seem to reject the need for ethical discussion. The
        authors argue that such blind optimism will cause irreversible
        damage to society if left unchecked. First we debunk the
        fallacious narratives people tend to tell about big data,
        offering a more realistic discussion of its merits and its
        limitations.  We then explore how analytical or algorithmic
        bias and sampling bias, two problems that statisticians have
        faced since long before the onset of big data, present
        pitfalls for deriving knowledge from data. We examine how the
        ethical implications of these pitfalls can cause serious
        damage in society. We conclude that Big Data analysis must
        obey the principles of transparency, clear and appropriate
        objective definition, and self-correcting feedback mechanisms.
    author:
        - Jones, Gabriel
        - Millard, Mathew
    hid:
        - 104
        - 216
    status: Nov 10 17 100%
    title: "Big Data = Big Bias? The Fallibility of Big Data"
    chapter: Theory
    url: https://github.com/bigdata-i523/hid104/tree/master/paper2
project:
    abstract: >
        While Big Data can make the world a better place, blind optimism 
        in its infallibility can cause irreversible damage to society if 
        left unchecked. With the mission of ensuring accountability, we 
        debunk the fallacious narratives people tend to tell about Big Data, 
        offering a more realistic discussion of its merits and its 
        limitations. We then explore how analytical or algorithmic bias and 
        sampling bias, two problems that statisticians have faced since long 
        before the onset of Big Data, present pitfalls for deriving knowledge 
        from data. We examine how the ethical implications of these pitfalls 
        can cause serious damage in society. We determine that effective, 
        credible, and ethically sound Big Data analysis must obey the 
        principles of transparency, clear and appropriate objective 
        definition, and self-correcting feedback mechanisms. We examine case 
        studies where academicians and businesses have tested algorithms to study
        how well they exhibit these principles. We then implement our own test to 
        check for potential algorithmic bias in Google. Based on evidence that 
        certain individuals have been corrupted in part by Google searches allegedly 
        bias against racial minorities, we hypothesize that Google's algorithms 
        systematically exhibit biases against minority groups. We test this hypothesis 
        by examining how Google search suggestions associate certain negative words 
        with names that typically belong to minority groups. We conclude that while 
        our study alone cannot prove or disprove our argument, the evidence in our 
        analysis contradicts our hypotheses, thus suggesting that no systematic bias 
        is exhibited. We discuss end by discussing what the results could mean for 
        future studies of potential algorithmic bias in Google.
    author:
        - Jones, Gabriel
        - Millard, Mathew
    hid:
        - 104
        - 216
    status: 100%
    title: Big Bias? An Analysis of Google Search Suggestions
    type: report
    url: https://github.com/bigdata-i523/hid104/tree/master/project
    chapter: Technology