A bubbling up theory is that AI biases are reflections of hidden societal power dynamics, which has vital impacts for AI and AI Ethics, including the use case of self-driving cars.
Understanding The Levels Of Self-Driving Cars As a clarification, true self-driving cars are ones where the AI drives the car entirely on its own and there isn’t any human assistance during the driving task.
These driverless vehicles are considered Level 4 and Level 5 , while a car that requires a human driver to co-share the driving effort is usually considered at Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-ons that are referred to as ADAS . There is not yet a true self-driving car at Level 5, and we don’t yet even know if this will be possible to achieve, nor how long it will take to get there. Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se . Since semi-autonomous cars require a human driver, the adoption of those types of cars won’t be markedly different than driving conventional vehicles, so there’s not much new per se to cover about them on this topic . For semi-autonomous cars, it is important that the public needs to be forewarned about a disturbing aspect that’s been arising lately, namely that despite those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car. You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3.For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task.One aspect to immediately discuss entails the fact that the AI involved in today’s AI driving systems is not sentient. In other words, the AI is altogether a collective of computer-based programming and algorithms, and most assuredly not able to reason in the same manner that humans can.Because I want to underscore that when discussing the role of the AI driving system, I am not ascribing human qualities to the AI. Please be aware that there is an ongoing and dangerous tendency these days to anthropomorphize AI. In essence, people are assigning human-like sentience to today’s AI, despite the undeniable and inarguable fact that no such AI exists as yet. With that clarification, you can envision that the AI driving system won’t natively somehow “know” about the facets of driving. Driving and all that it entails will need to be programmed as part of the hardware and software of the self-driving car.First, it is important to realize that not all AI self-driving cars are the same. Each automaker and self-driving tech firm is taking its approach to devising self-driving cars. As such, it is difficult to make sweeping statements about what AI driving systems will do or not do. Furthermore, whenever stating that an AI driving system doesn’t do some particular thing, this can, later on, be overtaken by developers that in fact program the computer to do that very thing. Step by step, AI driving systems are being gradually improved and extended. An existing limitation today might no longer exist in a future iteration or version of the system.We shall consider next how AI biases and societal power dynamics might come to play in this context of self-driving cars. Let’s start with one of the most transparent and frequently cited “power plays” regarding autonomous vehicles and especially self-driving cars. Some pundits are worried that self-driving cars will be the province of only the wealthy and the elite. It could be that the cost to use self-driving cars will be prohibitively expensive. Unless you’ve got big bucks, you might not ever see the inside of a self-driving car. Those that will be utilizing self-driving cars are going to have to be rich, it is purportedly contended . This could be construed as a form of societal power dynamics that will permeate the advent of AI-based self-driving cars. The overall autonomous vehicle industrial system as a whole will keep self-driving cars out of the hands of those that are poor or less affluent. This might not necessarily be by overt intent and just turns out that the only believed way to recoup the burdensome costs of having invented self-driving cars will be to charge outrageously high prices.Moving on, we can next consider the matter of AI-related statistical and computational biases. Contemplate the seemingly inconsequential question of where self-driving cars will be roaming to pick up passengers. This seems like an abundantly innocuous topic. At first, assume that AI self-driving cars will be roaming throughout entire towns. Anybody that wants to request a ride in a self-driving car has essentially an equal chance of hailing one. Gradually, the AI begins to primarily keep the self-driving cars roaming in just one section of town. This section is a greater money-maker and the AI has been programmed to try and maximize revenues as part of the usage in the community at large. Community members in the impoverished parts of the town turn out to be less likely to be able to get a ride from a self-driving car. This is because the self-driving cars were further away and roaming in the higher revenue part of the town. When a request comes in from a distant part of town, any other request from a closer location would get a higher priority. Eventually, the availability of getting a self-driving car in any place other than the richer part of town is nearly impossible, exasperatingly so for those that lived in those now resource-starved areas.You could assert that the AI pretty much landed on a form of statistical and computational biases, akin to a form of proxy discrimination . The AI wasn’t programmed to avoid those poorer neighborhoods. Instead, it “learned” to do so via the use of the ML/DL. See my explanation about proxy discrimination and AI biases atAlso, for more on these types of citywide or township issues that autonomous vehicles and self-driving cars are going to encounter, see my coverage atDo societal power dynamics come to play in this instance?For the third instance of AI biases, we turn to an example that involves the AI determining whether to stop for awaiting pedestrians that do not have the right-of-way to cross a street. You’ve undoubtedly been driving and encountered pedestrians that were waiting to cross the street and yet they did not have the right-of-way to do so. This meant that you had discretion as to whether to stop and let them cross. You could proceed without letting them cross and still be fully within the legal driving rules of doing so. Studies of how human drivers decide on stopping or not stopping for such pedestrians have suggested that sometimes the human drivers make the choice based on untoward biases. A human driver might eye the pedestrian and choose to not stop, even though they would have stopped had the pedestrian had a different appearance, such as based on race or gender. I’ve examined this atImagine that AI-based self-driving cars are programmed to deal with the question of whether to stop or not stop for pedestrians that do not have the right-of-way. Here’s how the AI developers might decide to program this task. They collect data from a town’s video cameras that are placed all around the city. The data showcases human drivers that do stop for pedestrians that do not have the right-of-way and also capture video of human drivers that do not stop. This is all collected into a large dataset. By using Machine Learning and Deep Learning, the data is modeled computationally. The AI driving system then uses this model to decide when to stop or not stop. Generally, the idea is that whatever the local custom consists of, this is how the AI is going direct the self-driving car. To the surprise of the city leaders and the residents, imagine their shock when the AI opts to stop or not stop based on the age of the pedestrian.Upon a closer review of the ML/DL training data and videos of human driver discretion, it turns out that many of the instances of not stopping entailed pedestrians that had a walking cane such as might be customary for an elder. Human drivers were seemingly unwilling to stop and let an elder cross the street, presumably due to the anticipated length of time that it might take for someone to make the journey. If the pedestrian looked like they could quickly dart across the street and minimize the waiting time of the driver, the human drivers were more amenable to letting the person cross. This got deeply buried into the AI driving system via the ML/DL having “discovered” this pattern in the training data and accordingly computationally dutifully modeled upon it. Once the ML/DL was downloaded into the self-driving cars via the use of OTA updates, the onboard AI driving system went ahead to use the newly provided ML/DL models. Here’s how it would work. The sensors of the self-driving car would scan the awaiting pedestrian, feed this data into the ML/DL model, and the model would emit to the AI whether to stop or continue. Any visual indication that the pedestrian might be slow to cross, such as the use of a walking cane, mathematically was being used to determine whether the AI driving system should let the awaiting pedestrian cross or not.Some final thoughts for now.First, for clarification, there is no one particularly arguing that we should somehow ignore AI biases and jump over into examining solely the societal power dynamics only. I mention this because there are some that seem to toss the baby out with the bathwater and leap entirely into the power elements as though the AI biases are inconsequential in comparison.Second, we do need to focus on AI biases. And, meanwhile, we should also be keeping our eyes and ears open for the societal power dynamics. Yes, we can do both at the same time, especially now that we know to be on the watch. I assure you that people experiencing the gorilla experiment are apt to be on their toes whenever they next see anything that might challenge their cognitive agility. Knowing that a gorilla can be there, though it might not necessarily appear, will ensure that you are watching for the potential appearance anyway.An AI bias can be construed as “hiding” a societal power dynamics issue Nor are all societal power dynamics issues necessarily embedded only into an AI bias An AI bias can be part of a collective of AI biases that are all related to a particular societal power dynamics issue A litany of societal power dynamics issues can be riddling just about any AI system since AI systems are a reflection of society and power dynamics all-toldOkay, here are a few more rules of thumb that might also get the mental juices going:When devising an AI system, ask whether and in what ways the societal power dynamics are shaping the AIConsider what mitigations might be appropriate regarding AI and societal power dynamicshas reminded me of the impacts that societal power can engender. I would wager a bet that you are familiar with one of the most famous or infamous lines about power, which per Lord Action goes like this: “Power tends to corrupt, and absolute power corrupts absolutely.”Society has to be on the up and up that AI is going to have a demonstrative impact on societal power dynamics. This seems blatantly obvious, perhaps, and yet there isn’t as much outward debate and outspoken discourse on the matter as you might think would seem warranted. The rise of AI Ethics considerations is, fortunately, moving society in that direction. Inexorably, if not begrudgingly, and an excruciating inch at a time. We can give the final word for now to the great Leonardo da Vinci and take a reflective moment to ponder these astutely wise words: “Nothing strengthens authority so much as silence.” Those that are venturing into the AI biases and societal power dynamics arena are trying to bring direct sunlight and rapt attention to an otherwise quiet or unassuming matter that would seemingly silently lay in wait. Let’s start talking, as Leonardo da Vinci would presumably be urging us to do.
Please follow us on Google to support usUnited States Latest News, United States Headlines
Similar News: You can also read news stories similar to this one that we have collected from other news sources.
Robotic Beehive Using AI To Save The Bees And Global Food SupplyBeewise delivers beekeeping at scale with Beehome, a newly invented beehive using precision robotics, computer vision and AI to help beekeepers fight the alarming decline in the global bee population.
Read more »
Robotic Beehive Using AI To Save The Bees And Global Food SupplyBeewise delivers beekeeping at scale with Beehome, a newly invented beehive using precision robotics, computer vision and AI to help beekeepers fight the alarming decline in the global bee population.
Read more »
The Pastry A.I. That Learned to Fight CancerIn Japan, an A.I. system designed to distinguish croissants from bear claws has turned out to be capable of identifying cancer cells.
Read more »
Is DeepMind's Gato AI really a human-level intelligence breakthrough?DeepMind’s Gato AI can play computer games, chat with humans and stack coloured blocks with a robot arm, leading to claims that it is a 'general intelligence' of the kind long-sought by AI researchers - but some experts say Gato just isn't that smart
Read more »
China’s world-first AI-powered drone carrier is being hailed as a new ‘marine species’China launched the world's first autonomous drone carrier on Wednesday and it is being hailed as a new 'marine species.'
Read more »
Robotic Beehive Using AI To Save The Bees And Global Food SupplyBeewise delivers beekeeping at scale with Beehome, a newly invented beehive using precision robotics, computer vision and AI to help beekeepers fight the alarming decline in the global bee population.
Read more »