Introduction: Democracy will win with improved artificial intelligence.
Democracy will win with improved artificial intelligence. Use of artificial intelligence in electoral races, the core of democracy will be a fundamental choice of our time. Can we improve our intelligent systems to be smarter in the choices they make about information we read to decide the future of our country?
Artificial intelligence is the imitation of human knowledge and processes by machines, most particularly computer systems. AI programming concentrates on three (3) cognitive skills: self-correction, reasoning, and learning.
Generally, Artificial intelligence is a multidisciplinary science with various approaches. However, improvements in deep learning and machine learning are creating a model shift in nearly all sectors of the tech industry.
Artificial intelligence systems typically demonstrate amazing behaviors associated with general human intelligence. This includes problem-solving, reasoning, manipulation, creativity, perception, learning, planning, motion, etc. This is why they can be useful in rooting out bias. This brings us to the next point.
Bias is an inevitable feature of life. It’s due to the significantly short view of the world any group or single person can attain, which influences their perception of events and ideas. In the hyper-partisan political environment, distinguishing real from fake is even more challenging than ever. It is worse when political bias is tossed in the mix, thus twisting opinions and facts. This bias then automatically leads to misinformation and conspiracy theories which is very deadly for us as a society.
It is critical for all of us to look into inherent bias that leads to misinformation thanks to artificial intelligence in the modern day democracy. Fair and unbiased representation of facts is an essential principle of modern democracy, as people vote or make up their minds to select a candidate based on these facts. Let us discuss how artificial intelligence can help reduce the problem it creates.
One good example is the misinformation about voting. How can you vote? where do you need to vote? can you vote by mail? Is there voter fraud?
Artificial intelligence, enforces these theories by surfacing content you are most likely to believe, and not based on the facts and reality. This bombardment of this misinformation grows exponentially and we don’t tend to differentiate between reality and misinformation.
We need to be concerned and attentive not only at the level of responsible design of artificial intelligence, but responsible for its use for swaying minds and gathering clicks for a better profit or worse, change the course of a country.
It is important we watch the documentary Social Dilemma on Netflix to understand the nuances of this subtle art to keep the users hooked on and what implications this might have in short term and long term. Please do watch the trailer by Netflix below –
How can artificial intelligence help reduce this bias and misinformation?
Democracy will win with improved artificial intelligence.
Diversifying Datasets, Teams, and Data Stream
There’s a need to build a dataset first to train AI to develop better models. A dataset is where we train artificial intelligence to understand its purpose better. Eventually, we can try out different datasets to discover the more accurate ones and eliminate statistical bias.
Generally, increasing dataset size will expose the machine learning model to a larger class of topics and data. Ultimately, this will help increase estimation accuracy. The advantage of artificial intelligence lies in its vast contextual knowledge of each medium it assesses. It can crowdsource social media, encyclopedias, URL structures, and web traffic data, all in a bid to decide trustworthiness.
Typically, the AI system only requires about 150 articles to determine a reliable source. It’s 65% accurate at identifying whether a media source has a low, medium, or high level of factuality. Also, it’s 70% accurate at determining whether it’s moderate, right-leaning, or left-leaning. It is extremely important that there is no under fitting or over fitting of data.
Using Different Models
Learning is one of the primary building blocks of AI solutions. Right from a theoretical viewpoint, it’s a process that advances the principles of an AI system. From a technical perspective, the artificial learning process is centered on preparing a collection of input-output pairs. This can be used to foretell the outputs of new inputs or for a specific function.It is extremely important that we build a diverse set of data models that encompasses a wide variety of data points being pulled from trustworthy resources.
Building Better Data Processing for Artificial Intelligence
Ultimately, data is the core of any machine learning or AI algorithm. The primary function of AI algorithms and automation is to undo the concealed knowledge/information accessible in the data. As a result, if it can process data better, the results will be more precise. It is important to pull data that can be verified via an intelligent system with trustworthy sources. This clearly identifies the requirement of building a transparent open for all database of trustworthy resources which would be a common collective for all machine learning algorithms to pull the data from.
Building Better algorithms for Artificial Intelligence
An algorithm in AI and automation is a method that is run on data to build a machine learning model. Building better algorithms will help the system better identify accurate democracy or political bias and reduce the misinformation across the board. Because machine learning algorithms help machines become intelligent it is critical to have these algorithms right. We should be giving equal importance to fact checking algorithms and make sure they are interlinked with data verification algorithms. this gives us a better handling of end results that come out of these algorithms.
Building Different Architecture for Artificial Intelligence
Understanding which model of configuration and architecture works best in artificial intelligence and automation is the best way to enhance its accuracy. To build a more accurate model to forecast and categorize political or democracy bias, trying out different architecture is better. This may also include auditing the algorithm creation process to identify and eliminate the human-driven baseline biases that may come into play during development. Adding an element of human intelligence in the architecture is vital for monitoring bias and misinformation. This network of human intelligence architecture should include diverse experts in various fields including, lawyers, teachers, professors, journalists.. etc.
Model configuration and architecture plays a significant role in the accuracy level of an AI system. We definitely need to improve upon that to help deal with political bias.
Conclusion – Democracy will win with improved artificial intelligence.
AI can be a powerful tool to empower public conversations and strengthen the quality of political debate by presenting news and content from across the political spectrum from verified sources to allow space for differing opinions and highlight cases of polarization and media bias. There is a case for leveraging natural language processing to scale the system even further.
Use of artificial intelligence in electoral races, the core of democracy will be a fundamental choice of our time. Can we improve our intelligent systems to be smarter in the choices they make about information we read to decide the future of our country? Democracy will win with improved artificial intelligence and larger public participation.
Introduction: Democracy will win with improved artificial intelligence.
Democracy will win with improved artificial intelligence. Use of artificial intelligence in electoral races, the core of democracy will be a fundamental choice of our time. Can we improve our intelligent systems to be smarter in the choices they make about information we read to decide the future of our country?
Artificial intelligence is the imitation of human knowledge and processes by machines, most particularly computer systems. AI programming concentrates on three (3) cognitive skills: self-correction, reasoning, and learning.
Generally, Artificial intelligence is a multidisciplinary science with various approaches. However, improvements in deep learning and machine learning are creating a model shift in nearly all sectors of the tech industry.
Artificial intelligence systems typically demonstrate amazing behaviors associated with general human intelligence. This includes problem-solving, reasoning, manipulation, creativity, perception, learning, planning, motion, etc. This is why they can be useful in rooting out bias. This brings us to the next point.
Table of contents
Also Read: Artificial Intelligence and disinformation.
AI and Misinformation
We spoke to the founder of aiplusinfo.com, Sanksshep Mahendra, on AI and Misinformation.
What is Bias?
Bias is an inevitable feature of life. It’s due to the significantly short view of the world any group or single person can attain, which influences their perception of events and ideas. In the hyper-partisan political environment, distinguishing real from fake is even more challenging than ever. It is worse when political bias is tossed in the mix, thus twisting opinions and facts. This bias then automatically leads to misinformation and conspiracy theories which is very deadly for us as a society.
It is critical for all of us to look into inherent bias that leads to misinformation thanks to artificial intelligence in the modern day democracy. Fair and unbiased representation of facts is an essential principle of modern democracy, as people vote or make up their minds to select a candidate based on these facts. Let us discuss how artificial intelligence can help reduce the problem it creates.
One good example is the misinformation about voting. How can you vote? where do you need to vote? can you vote by mail? Is there voter fraud?
Artificial intelligence, enforces these theories by surfacing content you are most likely to believe, and not based on the facts and reality. This bombardment of this misinformation grows exponentially and we don’t tend to differentiate between reality and misinformation.
We need to be concerned and attentive not only at the level of responsible design of artificial intelligence, but responsible for its use for swaying minds and gathering clicks for a better profit or worse, change the course of a country.
It is important we watch the documentary Social Dilemma on Netflix to understand the nuances of this subtle art to keep the users hooked on and what implications this might have in short term and long term. Please do watch the trailer by Netflix below –
How can artificial intelligence help reduce this bias and misinformation?
Democracy will win with improved artificial intelligence.
Diversifying Datasets, Teams, and Data Stream
There’s a need to build a dataset first to train AI to develop better models. A dataset is where we train artificial intelligence to understand its purpose better. Eventually, we can try out different datasets to discover the more accurate ones and eliminate statistical bias.
Generally, increasing dataset size will expose the machine learning model to a larger class of topics and data. Ultimately, this will help increase estimation accuracy. The advantage of artificial intelligence lies in its vast contextual knowledge of each medium it assesses. It can crowdsource social media, encyclopedias, URL structures, and web traffic data, all in a bid to decide trustworthiness.
Typically, the AI system only requires about 150 articles to determine a reliable source. It’s 65% accurate at identifying whether a media source has a low, medium, or high level of factuality. Also, it’s 70% accurate at determining whether it’s moderate, right-leaning, or left-leaning. It is extremely important that there is no under fitting or over fitting of data.
Using Different Models
Learning is one of the primary building blocks of AI solutions. Right from a theoretical viewpoint, it’s a process that advances the principles of an AI system. From a technical perspective, the artificial learning process is centered on preparing a collection of input-output pairs. This can be used to foretell the outputs of new inputs or for a specific function.It is extremely important that we build a diverse set of data models that encompasses a wide variety of data points being pulled from trustworthy resources.
Also Read: Artificial intelligence in Journalism.
Building Better Data Processing for Artificial Intelligence
Ultimately, data is the core of any machine learning or AI algorithm. The primary function of AI algorithms and automation is to undo the concealed knowledge/information accessible in the data. As a result, if it can process data better, the results will be more precise. It is important to pull data that can be verified via an intelligent system with trustworthy sources. This clearly identifies the requirement of building a transparent open for all database of trustworthy resources which would be a common collective for all machine learning algorithms to pull the data from.
Also Read: Self taught AI will be the end of us.
Building Better algorithms for Artificial Intelligence
An algorithm in AI and automation is a method that is run on data to build a machine learning model. Building better algorithms will help the system better identify accurate democracy or political bias and reduce the misinformation across the board. Because machine learning algorithms help machines become intelligent it is critical to have these algorithms right. We should be giving equal importance to fact checking algorithms and make sure they are interlinked with data verification algorithms. this gives us a better handling of end results that come out of these algorithms.
Also Read: AI and Weapons Of The Future
Building Different Architecture for Artificial Intelligence
Understanding which model of configuration and architecture works best in artificial intelligence and automation is the best way to enhance its accuracy. To build a more accurate model to forecast and categorize political or democracy bias, trying out different architecture is better. This may also include auditing the algorithm creation process to identify and eliminate the human-driven baseline biases that may come into play during development. Adding an element of human intelligence in the architecture is vital for monitoring bias and misinformation. This network of human intelligence architecture should include diverse experts in various fields including, lawyers, teachers, professors, journalists.. etc.
Model configuration and architecture plays a significant role in the accuracy level of an AI system. We definitely need to improve upon that to help deal with political bias.
Conclusion – Democracy will win with improved artificial intelligence.
AI can be a powerful tool to empower public conversations and strengthen the quality of political debate by presenting news and content from across the political spectrum from verified sources to allow space for differing opinions and highlight cases of polarization and media bias. There is a case for leveraging natural language processing to scale the system even further.
Use of artificial intelligence in electoral races, the core of democracy will be a fundamental choice of our time. Can we improve our intelligent systems to be smarter in the choices they make about information we read to decide the future of our country? Democracy will win with improved artificial intelligence and larger public participation.
References
“Dealing With Bias in Artificial Intelligence (Published 2019).” The New York Times, https://www.nytimes.com/svc/oembed/html/?url=https%3A%2F%2Fwww.nytimes.com%2F2019%2F11%2F19%2Ftechnology%2Fartificial-intelligence-bias.html#? Accessed 3 June 2023.
Mahendra, Sanksshep. “Automation — Future of Cybersecurity.” Artificial Intelligence +, 28 Aug. 2019, https://www.aiplusinfo.com/blog/artificial-intelligence-automation-future-of-cybersecurity/. Accessed 3 June 2023.
Wang, Welton. “Calculating Political Bias and Fighting Partisanship with AI.” The Bipartisan Press, 22 Dec. 2019, https://www.thebipartisanpress.com/politics/calculating-political-bias-and-fighting-partisanship-with-ai/. Accessed 3 June 2023.
Wiggers, Kyle. “MIT CSAIL’s AI Can Detect Fake News and Political Bias.” VentureBeat, 4 Oct. 2018, https://venturebeat.com/2018/10/03/mit-csails-ai-can-detect-fake-news-and-political-bias. Accessed 3 June 2023.
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