3 ms·

I used to think that unbiased news is an intractable problem purely based on information theory: to have an unbiased picture of the world, you need to have all

I used to think that unbiased news is an intractable problem purely based on information theory: to have an unbiased picture of the world, you need to have all the facts, which is physically impossible; and any layer that simplifies those facts (be it a journalist or an LLM) will add bias (either by journalist, journalist’s employer, LLM’s training data or operator preferences, and so on). In short, when your selection of facts to report is already biased, there’s not much you can do. In even shorter, map is not the territory (and all you have is a map). However, I realized that’s not really true. I mean, of course you cannot have unbiased news, and reevaluating the definition of “news” is not going to help you. It’s just that there is actually a very good mechanism for combatting this—and it’s called “be aware of your bias and upfront about it”. There is any other way around it.

echelon 4y ago
Unbiased news algorithm: 1. Identify all clusters of power and opinion, eg. Republicans, Democrats, Chinese Communist Party, UK Labour Party, Green Party, etc. 2. Associate each cluster with news organizations that tightly align with their policies. 3. Evaluate stories - headlines in only one cluster (under reported elsewhere) represent a view. Headlines that have different takes, likewise, give you a spectrum of thought on an issue or topic. 4. Collect fine points and build a report. Bonus for providing contrasting opinions and associating them with the party/power responsible for that thinking. I want this tool. It'd be okay if it wasn't perfect. Just getting 80% of the way there would be incredibly valuable. I'd pay for it.
- netsroht 4y ago
  When I started working on Zeitgaist [1] I immediately recognized that biased information is the main problem. I'm currently thinking about attaching additional information to the sources I present like ground [2] is doing with political spectrums. I really like your idea for the news algorithm. If you or someone elsw wants to build something like that on top of Zeitgaist with me, don't hesitate to contact me. [1] https://zeitgaist.ai https://zeitgaist.ai [2] https://ground.news/ https://ground.news/
- mynameishere 4y ago
  Your "etc" includes some very powerful ethnic groups. Are you planning on "identifying" them? I didn't think so.
  - echelon 4y ago
    I don't see why you can't. Throw religion in while you're at it. Even interest groups around other classes: disability, socioeconomic, etc. You can absolutely say a newspaper (or individual columnist or editor) is pro-LGBT, pro-Israel, pro-Palestine, pro-Mormon, pro-Amish, pro-Atheist, etc. That shouldn't be controversial. Wikipedia can and does do this in a relatively neutral way. If your classification is meant to disparage a group, then I'd see the trouble. Don't do that. I want an up to date, well rounded view of how everyone sees the world. A system capable of getting me to see geopolitical events in other perspectives is useful, even if I ultimately choose to uphold my existing beliefs. FWIW, I subscribe to all sorts of diverse political view subreddits and Twitter handles. Unfortunately, it's still hardly comprehensive is a lot of work to analyze and distill.
    - ethbr0 4y ago
      What we're really talking about here is boting any deviation from a tabula rasa appraisal of reality, then clustering that into an affiliation/quality, right? I.e. Christians (as an averaged whole) can look at the same situation as a blank slate and have a statistically significant and consistently different perspective As can scientists. As can maybe microbiologists and industrial chemists. The point of the clustering isn't that the tags are pejorative, but that they're predictive metadata on any source's bias. And often times that's currently unexposed. I see a post by echelon, but only by the content of that single post (and maybe some prior HN contact) can I reconstruct his typical perspectives.
  - pydry 4y ago
    It would probably include some institutions masquerading as representatives of ethnic groups but that isnt quite the same thing.
- strogonoff 4y ago
  1. Will your cluster identification be unbiased? How do you avoid providing more fine-grained taxonomy for clusters that hit close to home for you (or this tool’s operator) and shoving the rest into as few buckets as possible (because you don’t care)? 2. What about biases shared by all news organizations, regardless of cluster?
blackbear_ 4y ago
Technically speaking, if every day you take an uniformly random selection of news articles, on average what you read will not be biased. Or, to be precise, it will follow the same biases as the population of news articles you sample from.
- denton-scratch 4y ago
  > what you read will not be biased. Or to be precise [...] Your "to be precise" sentence states directly that what you read WILL be biased.
- strogonoff 4y ago
  Replace “news articles” with “facts” and you’re closer to the point, but then how useful would that input be to you?
  - ethbr0 4y ago
    The difference is that facts are harder to consume than news articles, as the latter is essentially packaged and distributed collections of the former. Furthermore, the intent of parent's bias averaging, as I understand it, is... 1) There are a finite number of major biases axes in mass media (e.g. political parties, wealthy-poor, conservative-liberal, etc.), ergo averaging these out is a helpful reduction of major biases 2) It assumes the underlying material has a bias, and seeks to counteract it. If you build a fact-based method, you leave yourself open to treating biased opinions as facts, if they're sufficiently masked
    - strogonoff 4y ago
      Indeed, the first challenge to unbiased approach would be the impossible in practical terms task of separating facts from fiction. I don’t know if it is clear but I really meant it that unbiased news is not a possibility due to fundamental limitations of information theory and finiteness of individual information processing capabilities; self-awareness and bias transparency is the only way. > helpful reduction of major biases Only if those major biases are opposite between media. Imagine biases as vectors pointing in arbitrary directions. Best case is when bias vector in one cluster is compensated by exactly opposite bias vector in another cluster, but how often do you expect that to happen? A more typical scenario is a vector pointing in merely a different, rather than opposite, direction. A worst-case scenario, and also common (because bias manifests itself in selection if facts to report, and most sources report on the same events), is when all bias vectors point in a similar direction.
strogonoff 4y ago
To add: where I wrote “be aware of your bias and upfront about it” I of course meant journalists and news organizations. Corollary: since this awareness of the bias requires self-honesty and introspection, it’s drastically more difficult with an LLM because you also need to know all about your LLM developer and operator’s accidental and intentional biases.

echelon 4y ago

Unbiased news algorithm: 1. Identify all clusters of power and opinion, eg. Republicans, Democrats, Chinese Communist Party, UK Labour Party, Green Party, etc. 2. Associate each cluster with news organizations that tightly align with their policies. 3. Evaluate stories - headlines in only one cluster (under reported elsewhere) represent a view. Headlines that have different takes, likewise, give you a spectrum of thought on an issue or topic. 4. Collect fine points and build a report. Bonus for providing contrasting opinions and associating them with the party/power responsible for that thinking. I want this tool. It'd be okay if it wasn't perfect. Just getting 80% of the way there would be incredibly valuable. I'd pay for it.

netsroht 4y ago
When I started working on Zeitgaist [1] I immediately recognized that biased information is the main problem. I'm currently thinking about attaching additional information to the sources I present like ground [2] is doing with political spectrums. I really like your idea for the news algorithm. If you or someone elsw wants to build something like that on top of Zeitgaist with me, don't hesitate to contact me. [1] https://zeitgaist.ai https://zeitgaist.ai [2] https://ground.news/ https://ground.news/
mynameishere 4y ago
Your "etc" includes some very powerful ethnic groups. Are you planning on "identifying" them? I didn't think so.
- echelon 4y ago
  I don't see why you can't. Throw religion in while you're at it. Even interest groups around other classes: disability, socioeconomic, etc. You can absolutely say a newspaper (or individual columnist or editor) is pro-LGBT, pro-Israel, pro-Palestine, pro-Mormon, pro-Amish, pro-Atheist, etc. That shouldn't be controversial. Wikipedia can and does do this in a relatively neutral way. If your classification is meant to disparage a group, then I'd see the trouble. Don't do that. I want an up to date, well rounded view of how everyone sees the world. A system capable of getting me to see geopolitical events in other perspectives is useful, even if I ultimately choose to uphold my existing beliefs. FWIW, I subscribe to all sorts of diverse political view subreddits and Twitter handles. Unfortunately, it's still hardly comprehensive is a lot of work to analyze and distill.
  - ethbr0 4y ago
    What we're really talking about here is boting any deviation from a tabula rasa appraisal of reality, then clustering that into an affiliation/quality, right? I.e. Christians (as an averaged whole) can look at the same situation as a blank slate and have a statistically significant and consistently different perspective As can scientists. As can maybe microbiologists and industrial chemists. The point of the clustering isn't that the tags are pejorative, but that they're predictive metadata on any source's bias. And often times that's currently unexposed. I see a post by echelon, but only by the content of that single post (and maybe some prior HN contact) can I reconstruct his typical perspectives.
- pydry 4y ago
  It would probably include some institutions masquerading as representatives of ethnic groups but that isnt quite the same thing.
strogonoff 4y ago
1. Will your cluster identification be unbiased? How do you avoid providing more fine-grained taxonomy for clusters that hit close to home for you (or this tool’s operator) and shoving the rest into as few buckets as possible (because you don’t care)? 2. What about biases shared by all news organizations, regardless of cluster?

blackbear_ 4y ago

Technically speaking, if every day you take an uniformly random selection of news articles, on average what you read will not be biased. Or, to be precise, it will follow the same biases as the population of news articles you sample from.

denton-scratch 4y ago
> what you read will not be biased. Or to be precise [...] Your "to be precise" sentence states directly that what you read WILL be biased.
strogonoff 4y ago
Replace “news articles” with “facts” and you’re closer to the point, but then how useful would that input be to you?
- ethbr0 4y ago
  The difference is that facts are harder to consume than news articles, as the latter is essentially packaged and distributed collections of the former. Furthermore, the intent of parent's bias averaging, as I understand it, is... 1) There are a finite number of major biases axes in mass media (e.g. political parties, wealthy-poor, conservative-liberal, etc.), ergo averaging these out is a helpful reduction of major biases 2) It assumes the underlying material has a bias, and seeks to counteract it. If you build a fact-based method, you leave yourself open to treating biased opinions as facts, if they're sufficiently masked
  - strogonoff 4y ago
    Indeed, the first challenge to unbiased approach would be the impossible in practical terms task of separating facts from fiction. I don’t know if it is clear but I really meant it that unbiased news is not a possibility due to fundamental limitations of information theory and finiteness of individual information processing capabilities; self-awareness and bias transparency is the only way. > helpful reduction of major biases Only if those major biases are opposite between media. Imagine biases as vectors pointing in arbitrary directions. Best case is when bias vector in one cluster is compensated by exactly opposite bias vector in another cluster, but how often do you expect that to happen? A more typical scenario is a vector pointing in merely a different, rather than opposite, direction. A worst-case scenario, and also common (because bias manifests itself in selection if facts to report, and most sources report on the same events), is when all bias vectors point in a similar direction.

strogonoff 4y ago

To add: where I wrote “be aware of your bias and upfront about it” I of course meant journalists and news organizations. Corollary: since this awareness of the bias requires self-honesty and introspection, it’s drastically more difficult with an LLM because you also need to know all about your LLM developer and operator’s accidental and intentional biases.