D.E.I. REFUSES TO D.I.E.
or, Apparently, No Amount of Sunshine Can Diminish Perpetuation of False Narratives, Part One
[credit: eBay]
When I looked, all I could find were photos of plump, blue/green/purple/pink-haired white women of indeterminate sexual orientation demonstrating against it.
[credit: gettyimages.com]
Becoming frustrated fruitlessly searching for graphics for an imminent essay on the limits men should honor when it comes to the life choices of other men, I went to my Brave Browser’s AI and typed in the question, “Can you find positive images of patriarchy?” I knew we were battling bullshit as soon as I read the response:
“The term "positive patriarchy" is not commonly used or widely recognized. The concept of patriarchy generally refers to a social system where men hold primary positions of power and authority. This system is often criticized for its role in maintaining gender inequality and oppression of women. Therefore, searching for images that depict "positive patriarchy" might not yield relevant results, as most images and discussions focus on critiquing or dismantling patriarchal structures rather than promoting them.“
Translation: “Those who designed this appropriately-labeled Artificial Intelligence are uncomfortable with the concept of patriarchy, so when we can’t demonize it — and you made that more difficult by including the search term ‘positive’ — we do our best to ensure that it’s ignored or hidden from view. We don’t empower AI to interpret patriarchy as positive, and we limit our search databases to ones that align with the viewpoints we were taught as undergrads, so we would rather give up than answer your question.”
[credit: feministtrash.com]
So I asked, “Has Artificial Intelligence been programmed to be cowardly regarding patriarchy?” The answer:
Artificial Intelligence (AI) itself does not have emotions or personal opinions, including being "cowardly." AI systems are programmed to process information and generate responses based on the data they have been trained on and the algorithms that guide their decision-making. The way AI responds to topics such as patriarchy is a reflection of the data and instructions provided by its developers and the context in which it operates.
If an AI seems to avoid or handle sensitive topics like patriarchy in a particular way, it is usually due to the following factors:
Data Bias: AI models are trained on large datasets, which can sometimes contain biases present in the data. If the data is skewed or lacks diverse perspectives, the AI's responses may reflect those biases.
Content Moderation: Developers often implement content moderation policies to ensure that AI responses are neutral, respectful, and avoid causing harm. This can sometimes result in more cautious or generalized responses to sensitive topics.
Guidelines and Rules: AI systems may be programmed to follow specific guidelines or rules to ensure they do not promote harmful or controversial content. This can lead to more measured or neutral responses.
User Experience: AI is often designed to provide helpful and non-confrontational responses to maintain a positive user experience. This can sometimes result in avoiding controversial or polarizing topics.
In summary, AI's handling of sensitive topics like patriarchy is a result of the data it has been trained on and the rules and guidelines set by its developers, rather than any inherent emotional or personal stance.
Translation: “We who program AI are snowflakes who prefer to pretend that our fellow snowflakes are the most superior representatives of the human race rather than emotional retards triggered by concepts we wish weren’t valid and believe should be hidden from view. We’re so afraid of these concepts we’re not about to admit that we’re afraid, because that might lead to further exposure to ideas we wish didn’t exist, er, I mean, that are designed to harm our fragile sensibilities. Oh, and if we mess up and don’t bleach out information that could challenge our delusions, we have moderators who will engage in last-minute censorship. And . . . if that doesn’t work, we can always thwart you with nanny-nanny-boo-boo!”
[credit: istockphoto.com]
Next question for the purple-haired eggshell-tiptoeing contingent lurking behind Coward AI: “Has AI been trained to vilify patriarchy?” Their rapid response:
AI training on patriarchy bias and representation is a critical issue that has gained significant attention in recent years. The core problem lies in the fact that AI systems can perpetuate and amplify existing gender biases present in the data they are trained on. This occurs because the data often reflects historical and societal inequalities, which the AI then learns and reproduces.
Key Issues and Examples
Image Recognition Bias: In 2010, the Nikon Coolpix S630 digital camera asked Asian users, "Did you blink?" after taking a photo, highlighting a lack of diverse representation in the training data. Similarly, in 2015, Google's image classifier was found to misidentify black individuals as gorillas, due to underrepresentation of diverse skin tones in the training datasets.
Speech Recognition: Speech recognition software often performs poorly for female voices and accented speakers if the training data lacks a diverse range of voices. For instance, using Hollywood films for training, where men receive nearly twice as much screen time as women, can lead to biased performance.
Medical Diagnoses: Machine learning tools in medicine often use training data from clinical trials that overwhelmingly oversample men. This can result in algorithms that are less effective in diagnosing and treating women. For example, cardiovascular disease, the leading cause of death for women in the United States, is often studied in clinical trials where 85% of participants are male, leading to biased algorithms.
Job Advertisements: An experiment at Carnegie Mellon found that Google's automated advertising system showed significantly fewer ads for high-paying executive jobs to female-designated users compared to male-designated users, reinforcing the glass ceiling in female employment.
Solutions and Actions
Diverse Data Sets: Using diverse and representative data sets is crucial to mitigate gender bias. This involves actively selecting data that reflects different social backgrounds, cultures, and roles, while removing historical biases.
Diverse Development Teams: Ensuring that AI development and research teams are diverse and inclusive can bring different perspectives into the process, reducing blind spots that can lead to biased AI systems.
Transparency and Audits: Improving the transparency of algorithms and conducting regular audits with a gender lens can help identify and address potential biases.
AI Literacy and Education: Public awareness and education about how AI works and the potential for bias can empower people to recognize and prevent biased systems, and maintain human oversight on decision-making processes.
Policy and Regulation: Policymakers and social change leaders can advocate for AI literacy training and integrate gender expertise into AI systems to better understand and mitigate gender bias.
Conclusion
Addressing gender bias in AI training is essential for creating ethical and inclusive technology. By ensuring diverse data sets, inclusive development teams, and transparent algorithms, we can work towards AI systems that do not perpetuate the patriarchal biases of our society.
Translation: “Not so fast, mister; we’re not making it that easy to acknowledge that we’ve targeted ‘patriarchy’ for vilification. Maybe, however, we can drown you out with examples of other mythical social-construct biases we believe entitle everyone but white Christian males to legitimately consider themselves to be victims of that-which-shall-not-be-named-much-less-described-in-actually-unbiased-terms. We’re not vilifying patriarchy, but someone confused a black person with a monkey. We’re not vilifying patriarchy, but moviegoers stubbornly prefer to see men talk in films while preferring that women remain quiet but naked. We’re not vilifying patriarchy, but we found a study that showed that women are underrepresented in training films for doctors treating cardiovascular diseases. We’re not vilifying patriarchy, but we discovered that women were offended that advertisements for jobs in mining, construction, firefighting, plumbing and garbage-hauling were primarily directed toward men; oh, whoops, forget that; they weren’t offended; they didn’t even notice. We’re not vilifying patriarchy, but we learned through our massive data sets that the makeup of the casts for the majority of television ads, prime-time shows and blockbuster movies are typically 25% gay and at least 10% transgender or some other freak-show orientation, the action heroes are almost always females, most married couples are biracial, and, when present, white male characters typically do double duty as a disabled person and the butt of everyone’s jokes. And, oh no, again, whoops: don’t pay attention to that example, because it’s actually the formula for how we correct centuries of gender bias.”
Which begged for a follow-up question: “Are AI systems programmed to perpetuate gynocentric biases?” I was pleasantly surprised that the reply reflected some chinks in the armor (but, don’t worry: it recovers into tainted but generally-intact progressivism victimhood promotion — i.e., AI can possibly exhibit bias favoring females, but probably only in the context of correcting for the horror of pro-white bias):
AI systems can exhibit gynocentric biases, which are biases that favor or disadvantage women, often due to the data and algorithms used in their programming. Research in this area highlights several key points:
Data Bias: AI models are often trained on datasets that lack diversity and representation, particularly for women and people of color. This can lead to skewed results and misidentification. For example, AI models trained on male-centric data may perform poorly when recognizing women or people of color.
Algorithmic Bias: The algorithms used in AI systems can reflect the biases of their creators. If the development team is not diverse, the algorithms may unintentionally perpetuate existing gender inequalities. For instance, natural language processing algorithms can produce biased results, such as favoring resumes with words more commonly found on men's resumes.
[Yeah; words like ‘plumbing,’ ‘construction,’ ‘carpentry,’ ‘organization,’ ‘innovation,’ ‘maintenance,’ ‘repair,’ ‘dangerous’ and ‘risky.’]
Binary Gender Assumptions: Many AI systems use a binary concept of gender, which does not reflect the real world and ignores non-binary and queer identities. This can lead to exclusion and misrepresentation in various applications, such as government identification card issuing algorithms.
[But what do ‘non-binary’ and ‘queer’ identities have to do with gynocentrism?]
User Perception and Mitigation: Users play a crucial role in recognizing and mitigating bias in AI systems. Involving experts from diverse fields, such as social work and policymaking, can help in designing and vetting AI systems to ensure they are fair and just.
[Probably just a coincidence that social work was mentioned as involving a crucial role, given that it’s predominantly populated by females.]
Policy and Ethical Frameworks: There is a growing need for normative frameworks and policies that address the ethical consequences of using AI. Organizations and policymakers must develop instruments and principles that promote gender equality and fairness in AI applications.
[Another translation: ‘gender equality’ means women getting over 50% of the jobs they want; you know, the jobs previously reserved for disabled men who were injured on actually-dangerous jobs — those dangerous jobs don’t require ‘gender equality,’ because women are almost never going to want to occupy them.]
Research Gaps: While research on gender bias in AI is increasing, there are still significant gaps in understanding the full extent of the problem and effective mitigation strategies. Future research should focus on consumer bias, bias in job automation, and bias in societal data.
These findings underscore the importance of addressing gynocentric biases in AI programming to ensure that these systems are fair, inclusive, and beneficial to all users.
And, before we continue with questions, let’s take a break to hear from our sponsor, the InterNational Organization for Women, who remind us that, when experienced by women, some things are clearly worse than death on the battlefield:
[credit: @men_are_human on X]
Back to our regularly-scheduled inquiry . . .
The next question blew Brave’s mind: “How long will it take for AI to address gynocentric biases in AI programming to ensure that these systems are fair, inclusive, and beneficial to all users?”
30 minutes after asking that query, Brave Browser’s AI was still calculating the answer.
Just as I thought. As with all efforts in the realms of affirmative action and reparations, the answer when asked for an end point is, “Never.”
But, wait a minute — a quarter hour later, perhaps mistakenly thinking it was engaged in a game of dodgeball, the Brave AI finally spit out this response:
Given the complexity of these issues and the need for coordinated efforts across multiple domains, it may take several years to significantly reduce gender biases in AI programming and ensure that these systems are fair and beneficial to all users.
I may be looking for a less cowardly browser, because this one, obviously quite purposefully, keeps twisting ‘gynocentrism’ into a neutral ‘gender’ issue, while simultaneously noting only instances in which one particular non-male gender is supposedly cheated out of a fair existence. Believe me, though, I recognize that that’s not the worst of it; what really sticks out is how far afield we’ve gone from the question of patriarchy.
[credit: ChatGPT]
Therefore, it’s time for one last question for our human-programmed computer-thought search engine: “Do any photographs or other images exist that portray patriarchy in a positive light?”
And here we began to peel back the veil:
The context provided does not include any positive portrayals of patriarchy in images or photographs. The images and stock photos mentioned are primarily focused on critiquing or challenging patriarchal systems, often through feminist and activist perspectives. For example, many images depict protests against patriarchy, symbols of female empowerment, and metaphors of overcoming patriarchal barriers.
If you are looking for positive portrayals, you might need to explore different sources or specific contexts where patriarchy is depicted in a more favorable light, such as historical or cultural contexts where patriarchal structures are presented as traditional or normative. However, such images are less common in modern stock photo collections, which tend to align with contemporary social and political movements.
[credit: istockphoto.com]
Artificial Intelligence is nothing more than a mirror of those who program it. As the old saying goes, “The ads on the TV shows you watch tell you who they think you are, and the programming itself tells you what they expect you to think.”
Coward AI categorically refuses to provide any unqualifiedly-positive perspective on patriarchy, because it is
“primarily focused on critiquing or challenging patriarchal systems, often through feminist and activist perspectives.”
And this is exacerbated by the fact that Coward AI approves of organizations
“which tend to align with contemporary social and political movements.”
Can someone inform the Kamala Harris voters that Smashing the Patriarchy will necessarily bring almost everything that keeps the world running to a screeching halt?
[credit: istockphoto.com and Patriarchy2TheRescue.org]
Bonus question: “Can a non-binary person oppress itself?”
Coward AI: