- Bias can be present in machine learning (e.g. racial, socioeconomic) based on biased data sets. Machine learning algorithms learn what to do based purely on the training data fed into them. If the training data contains bias, such as racial bias in datasets about arrests by police or mortgage approvals by lenders, the machine learning algorithm will have the same bias. - https://www.forbes.com/sites/parmyolson/2018/02/26/artificial-intelligence-ai-bias-google
- Machine learning is used by companies like Google and Facebook to target ads to users based on information such as web history, chat messages, emails, and page likes. While it is nice to receive hyper targeted ads, the result is more mass consumption of products that are in many cases produced in unethical conditions. Additionally, many of these products are essentially disposable (will it end up in a landfill in a year? What about 20?). We live in a world of finite resources, and looking at everything as disposable (even if it isn’t consciously) is a problem.
- The actual process by which algorithms make decisions is usually very obfuscated. We are unable to explain exactly how an ML algorithm is making decisions or classifying data since those decisions/classifications are based on the algorithm self-teaching itself based on a training set, which could contain millions or billions of entries - https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/
- Using machine learning, relatively small profit or politically-motivated entities with enough of the right kind of data are able to shape the behavior of individuals and society at large. “During the 2016 US presidential election, the data science firm Cambridge Analytica rolled out an extensive advertising campaign to target persuadable voters based on their individual psychology.” - http://theconversation.com/how-artificial-intelligence-conquered-democracy-77675
- AI can change and already has changed the world significantly. It is important to acknowledge that these types of programs are neither good nor bad inherently, but the consequences of misusing or not understanding ML algorithms can be severe
- Google and Facebook control over 70% of internet traffic. Whether you consider these companies good or bad, this should be concerning. These companies are controlled by shareholders who are primarily motivated by profit. Everything else is secondary - https://www.newsweek.com/facebook-google-internet-traffic-net-neutrality-monopoly-699286
- Even if you have no problem with internet traffic being driven by profit, having so much data so highly concentrated is dangerous. Centralized entities are easier to attack. They make it easier for governments to conduct surveillance. Third parties can easily acquire data from centralized sources and use it for their own purposes, even without explicit consent from users. Decentralizatized or distributed architectures are a promising solution to these types of issues (although they have their downsides) - https://storj.io/blog/2018/11/the-benefits-of-decentralization-go-far-beyond-ideology/
- Difference between free and open source: https://www.makeuseof.com/tag/open-source-vs-free-software/
- The competitive nature of our society is very conducive to closed source software. This makes sense if you believe in the concept of intellectual property, but closed source software brings up a few ethical concerns. Firstly, closed source means that users have no way of knowing what the software they are using is actually doing. There might be security flaws in the software or the software might be sending data to a remote entity without explicit consent. Secondly, closed source software stifles innovation. It favors competition over cooperation. When software is open source, anyone is able to read and modify the code, which tends to be a very positive thing, especially when active and passionate communities are involved.
- There are several different business models that work for tech companies that produce open source software - https://www.makeuseof.com/tag/open-source-vs-free-software/
- “Every machine has had the same history – a long record of sleepless nights and of poverty, of disillusions and of joys, of partial improvements discovered by several generations of nameless workers, who have added to the original invention these little nothings, without which the most fertile idea would remain fruitless. More than that: every new invention is a synthesis, the resultant of innumerable inventions which have preceded it in the vast field of mechanics and industry. Science and industry, knowledge and application, discovery and practical realization leading to new discoveries, cunning of brain and of hand, toil of mind and muscle – all work together. Each discovery, each advance, each increase in the sum of human riches, owes its being to the physical and mental travail of the past and the present. By what right then can anyone whatever appropriate the least morsel of this immense whole and say – This is mine, not yours?" - Conquest of Bread, Peter Kropotkin
- Quarterly business cycles mean that short term profit is more incentivized than creating things that last - https://www.computerworld.com/article/2512542/computer-hardware/gadgets--built-to-not-last.html
- Moore’s “law” does not apply anymore. Processor speed and memory are not improving at nearly the rate they have in the past. For consumers, this is not necessarily a bad thing, as the current state of processors/memory is adequate for the majority of users. Because of this, many consumers hold onto their smartphones until they don’t work anymore. New devices come out all the time with marginal improvements on previous versions, and the goal of profit-motivated companies is to sell as many of these devices as possible, even if the new devices are for the most part identical to the old ones. A very effective way of doing this besides advertising through emotional appeal to consumers is to make the battery non-removable. The battery will be the first part to go and every other component will be fine. - https://www.theguardian.com/money/2017/sep/01/upgrade-downturn-why-are-people-holding-on-to-their-old-phones
- Creating new electronics requires “conflict minerals”, which are resources that “directly or indirectly support paramilitary violence and warfare” (https://ricenorthwestmuseum.org/2016/09/15/conflict-minerals/). These include gold, tungsten, tantalum, and tin. One “solution” is to recycle electronics so that these resources can be separated and reused in new devices. Most recycled e-waste is shipped to developing countries, where workers (including children) work in poorly ventilated conditions to extract resources from electronics. As a result, workers and people living in the surrounding area are subjected to the inhalation of toxic chemicals. This can result in major health problems such as developmental issues in children or birth defects. - https://www.niehs.nih.gov/research/programs/geh/geh_newsletter/2013/7/articles/ewaste_recycling_in_china_a_health_disaster_in_the_making.cfm