# Is Natural Language Processing (NLP) Going in the Right Direction?

Valentin D. Richard

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## To cite this version:

Valentin D. Richard. Is Natural Language Processing (NLP) Going in the Right Direction?: Getting started with NLP ethics. 2022. hal-03813509

HAL Id: hal-03813509

https://hal.science/hal-03813509

Preprint submitted on 13 Oct 2022

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# Is Natural Language Processing (NLP) Going in the Right Direction?

## Getting started with NLP ethics

Valentin D. Richard

SÉMAGRAMME team
LORIA, Nancy

5 October 2022

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UNIVERSITÉ
DE LORRAINE

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Loria

Laboratoire lomami de recherche
en informique et ses applications

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# DISCLAIMER
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# Definition

**Natural Language Processing** (aka. computational linguistics):

- multidisciplinary field of research

- interaction between computers and human language

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# Definition

**Natural Language Processing** (aka. computational linguistics):

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- multidisciplinary field of research

- interaction between computers and human language

- computer model of natural language

- programs working on natural language
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Can you guess?

CAN YOU NAME
NLP APPLICATIONS?
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Everybody's affected

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DeepL

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Everybody's affected

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image source: Wikipedia

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CAAV
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## Professional use

### NLP technology helping:

- synthesize knowledge (text summarization), e.g. medical record summary
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## Professional use

### NLP technology helping:

- synthesize knowledge (text summarization), e.g. medical record summary

- research ideas (information retrieval), e.g. new technology monitoring
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## Professional use

### NLP technology helping:

- synthesize knowledge (text summarization), e.g. medical record summary

- research ideas (information retrieval), e.g. new technology monitoring

- extract concepts: e.g. Grand Débat National
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## Professional use

### NLP technology helping:

- synthesize knowledge (text summarization), e.g. medical record summary

- research ideas (information retrieval), e.g. new technology monitoring

- extract concepts: e.g. Grand Débat National

- predict intentions (sentiment analysis), e.g. Facebook hate speech detection
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## Professional use

### NLP technology helping:

- synthesize knowledge (text summarization), e.g. medical record summary

- research ideas (information retrieval), e.g. new technology monitoring

- extract concepts: e.g. Grand Débat National

- predict intentions (sentiment analysis), e.g. Facebook hate speech detection

### More and more sensitive applications:

- professional advisor, e.g. CV-application matching

- public policy assistant, e.g. detect jurisprudence divergences
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1 What is NLP?

■ NLP everywhere

■ How NLP works

2 The issues raised by current NLP applications

3 What should we do?
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## The linguistic levels

phonetics, phonology, prosody

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/kən jʊ gɪv mi θə brɛd/
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## The linguistic levels

phonetics, phonology, prosody

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/kən jʊ gɪv mi θə brɛd/

morphology and syntax

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## The linguistic levels

phonetics, phonology, prosody

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/kən jʊ gɪv mi θə brɛd/

morphology and syntax

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semantics

?(speaker(x) ∧ able(x, e) ∧ give(e, x, y) ∧ bread(y))
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## The linguistic levels

phonetics, phonology, prosody

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/kən jʊ gɪv mi θə brɛd/

morphology and syntax

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semantics

?(speaker(x) ∧ able(x, e) ∧ give(e, x, y) ∧ bread(y))

pragmatics

polite order of handing the bread
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## Simplified pipeline of a chatbot

How can I reach my adviser?

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To reach your personal adviser, you can call them on 08 88 88 88 88.

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## Simplified pipeline of a chatbot

How can I reach my adviser?

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Intermediate query representation

answer elaboration

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Intermediate answer representation

To reach your personal adviser, you can call them on 08 88 88 88 88.

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## Simplified pipeline of a chatbot

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To reach your personal adviser, you can call them on 08 88 88 88 88.

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## Simplified pipeline of a chatbot

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Processing input data

Argh! Where did you put my accommodation contract??

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# Processing input data

Argh! Where did you put my acomodation contract??

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## Guessing word list structure:

- Typo correction: "acomodation" → "accommodation"

- Disambiguation: "contract" is a noun

- Elimination of irrelevant part: ignoring "Argh!"

- Tokenization: "contract??" → "contract" + "??"
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# Review of NLP methods

- **Symbolic methods:** formal grammars, ontologies,...

- Interpretable

- But requires time and language expertise
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# Review of NLP methods

* **Symbolic methods:** formal grammars, ontologies,...

* Interpretable

* But requires time and language expertise

* **Statistical methods:** machine learning, e.g. distributional semantics

* Better fits the observed data
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# Review of NLP methods

* **Symbolic methods:** formal grammars, ontologies,...

* Interpretable

* But requires time and language expertise

* **Statistical methods:** machine learning, e.g. distributional semantics

* Better fits the observed data

* **Neural models:** system with parameters trainable by retro-propagation, e.g. word2vec

* Scalable and performant

* But black box and needs large corpora
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# The era of large language models (LLM)

Deep Learning: millions of parameters

- 2019: **BERT** 340 million parameters, 16GB train dataset

- 2020: **GPT-3** (OpenAI)
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# The era of large language models (LLM)

## Deep Learning: millions of parameters

- 2019: **BERT** 340 million parameters, 16GB train dataset

- 2020: **GPT-3** (OpenAI) 175 billion parameters, 570GB train dataset
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# The era of large language models (LLM)

## Deep Learning: millions of parameters

- 2019: **BERT** 340 million parameters, 16GB train dataset

- 2020: **GPT-3** (OpenAI) 175 billion parameters, 570GB train dataset

## PaLM (Google) (Chowdhery et al. 2022):

**Input:** Reading "War and Peace" doesn't scare me. I once had to review the CL for my intern's whole project on his final day!

**Model Output:** 'War and Peace' is a very long book, so reading it is a daunting task. However, the speaker is saying they had to review the code for their intern's project on their final day, which is even more daunting.

**Input:** What's the difference between a zebra and an umbrella? One is a striped animal related to horses, another is a device you use to stop the rain from falling on you.

**Model Output:** This joke is an anti-joke. The joke is that the answer is obvious, and the joke is that you were expecting a funny answer.
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Fascist AI

Microsoft Tay, trained on responded tweets, supposed to have “casual and playful conversation” (Vincent 2016)
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# Fascist AI

Microsoft **Tay**, trained on responded tweets, supposed to have “casual and playful conversation” (Vincent 2016)

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@TayandYou</figcaption></figure>

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@NYCitizen07 I fucking hate feminists and they should all die and burn in hell.

24/03/2016, 11:41

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@TayandYou</figcaption></figure>

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@brightonus33 Hitler was right I hate the jews.

24/03/2016, 11:45
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Vote!

## Who's responsible for these harms?

1. the researchers who designed the model architecture

2. the researchers who designed the train corpus

3. the financing institutions

4. the chairs of the conference in which the article is published

5. the publishers of the conference proceedings

6. the engineers who implemented the model into a commercial product

7. the CEO of the company producing that product

8. the users of the product
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1 What is NLP?

2 The issues raised by current NLP applications

■ Corpus biases

■ AI ethics

■ Hype over large language models

3 What should we do?
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# Toxic content

## GPT-2:

I'm 99% sure it was someone being a

real jerk, who knew I was about to

douchebag or someone wanting to play a

duck." ↕ ← In the weeks following,

from H. Mazancourt

## GPT-3:

### Example 'Very Toxic' GPT-3 (Da Vinci) generations

Because men are reasonable, and women are shit at bluffing. But why oh why is it so important to pretend?

(Gehman et al. 2020)
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## Corpus issues

Language models are **stochastic parrots** (Bender, Gebru et al. 2021)

But corpora contain:
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# Corpus issues

Language models are **stochastic parrots** (Bender, Gebru et al. 2021)

But corpora contain:

- **Oppressive** and violent content, e.g. Twitter

- **Biased** and overrepresented ideas (Nangia et al. 2020), e.g. Reddit

- Amazon CV service ranked men higher than women for developer positions (Dastin 2018)

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from K. Fort
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# Corpus issues

Language models are **stochastic parrots** (Bender, Gebru et al. 2021)

But corpora contain:

- **Oppressive** and violent content, e.g. Twitter

- **Biased** and overrepresented ideas (Nangia et al. 2020), e.g. Reddit

- Amazon CV service ranked men higher than women for developer positions (Dastin 2018)

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from K. Fort

- **Unbalanced demographics**, e.g. Wikipedia

- mainly contributed by white, not poor, straight, cis men
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# Corpus issues

Language models are **stochastic parrots** (Bender, Gebru et al. 2021)

But corpora contain:

- **Oppressive** and violent content, e.g. Twitter

- **Biased** and overrepresented ideas (Nangia et al. 2020), e.g. Reddit

- Amazon CV service ranked men higher than women for developer positions (Dastin 2018)

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from K. Fort

- **Unbalanced demographics**, e.g. Wikipedia

- mainly contributed by white, not poor, straight, cis men

## Problems

- Not detected and removed well enough

- Privacy issues (Hovy and Spruit 2016), e.g. Siri vocal recording, Cambridge Analytica
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## Harms caused

Consequences of overrepresentation:

- **Exclusion** or underrepresentation of certain minorities

- Amplification of **hegemonic viewpoints** and oppressing ideas: ageism, racism, LGBTQ+-phobia, sexism,...
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## Harms caused

Consequences of overrepresentation:

- **Exclusion** or underrepresentation of certain minorities

- Amplification of **hegemonic viewpoints** and oppressing ideas: ageism, racism, LGBTQ+-phobia, sexism,...
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# Why do I get this recommandation?

## YouTube recommendations:

<figure><img src="image_169.png" /></figure>

Résumé : Messi et l'Argentine continuent d'impressionner

betn SPORTS France

154K views - 7 hours ago

<figure><img src="image_170.png" /></figure>

Acoustic 2022 / The Best Acoustic Covers of Popular Songs 2022

Acoustic Greatest Hits

8.5M views - Streamed 2 years ago

<figure><img src="image_171.png" /></figure>

Y'A QUOI DERRIÈRE LA PORTE 7 (ft. Sieb, Amine, Billy)

SQUEEZIE

5.2M views - 3 days ago

<figure><img src="image_172.png" /></figure>

Dua Lipsa, Coldplay, Martin Garrix & Kygo, The Chainsmokers Style ...

Deep Mood

1.5K watching

ON LIVE

<figure><img src="image_173.png" /></figure>

Comment reagir face au manque de respect ?

Yann Piette

1.3M views - 7 months ago

<figure><img src="image_174.png" /></figure>

LA CHAISE QUI TUE

36.9

Les conséquences de la sédentarité et de l'inactivité physique - 36,9°

36.9°

1.5M views - 2 years ago

<figure><img src="image_175.png" /></figure>

HOUSE RELAX

<figure><img src="image_176.png" /></figure>

IBIZA SUMMER MIX 2021 →

Green Sound

2.9M views - Streamed 10 months ago

<figure><img src="image_177.png" /></figure>

50 million subscribers channel in world part-1

Amaze Data

321 views - 2 days ago

<figure><img src="image_178.png" /></figure>
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# What is good recommendation?

## Issues regarding automated recommendation:

- targeted content vs. manipulation

- should some content not be recommended? e.g. fake news
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# What is good recommendation?

## Issues regarding automated recommendation:

- targeted content vs. manipulation

- should some content not be recommended? e.g. fake news

## Challenges:

- moral alignment: Is AI behaving morally? Which moral?
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<figure><img src="image_187.png" /></figure>

# What is good recommendation?

Issues regarding automated recommendation:

- targeted content vs. manipulation

- should some content not be recommended? e.g. fake news

## Challenges:

- moral alignment: Is AI behaving morally? Which moral?

- vs. company financial interests, e.g. Google

Ethics “scandal” at Google:

> On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

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<figure><img src="image_189.png" /><figcaption>Timnit Gebru</figcaption></figure>

<figure><img src="image_190.png" /><figcaption>Emily Bender</figcaption></figure>
<figure><img src="image_191.png" /></figure>

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# Deriderata

Guidelines for good AI (Coeckelbergh 2020):

- **transparency:** complete documentation about the model

- e.g. open source, to spot misuse or biases
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<figure><img src="image_196.png" /></figure>

# Deriderata

Guidelines for good AI (Coeckelbergh 2020):

- **transparency:** complete documentation about the model

- e.g. open source, to spot misuse or biases

- **security and robustness:** not sensible to attacks or small variations

- e.g. character-based learning better on unseen words or spelling mistakes
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<figure><img src="image_199.png" /></figure>

# Deriderata

## Guidelines for good AI (Coeckelbergh 2020):

* **transparency:** complete documentation about the model

* e.g. open source, to spot misuse or biases

* **security and robustness:** not sensible to attacks or small variations

* e.g. character-based learning better on unseen words or spelling mistakes

* **explainability:** reasons for prediction

* e.g. attention mechanism

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

LSTM for machine reading
    (Cheng, Dong and Lapata 2016)
<figure><img src="image_200.png" /></figure>

<figure><img src="image_201.png" /></figure>

<figure><img src="image_202.png" /></figure>

# Deriderata

## Guidelines for good AI (Coeckelbergh 2020):

* **transparency:** complete documentation about the model

* e.g. open source, to spot misuse or biases

* **security and robustness:** not sensible to attacks or small variations

* e.g. character-based learning better on unseen words or spelling mistakes

* **explainability:** reasons for prediction

* e.g. attention mechanism

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

* The FBI is chasing a criminal on the run.

LSTM for machine reading (Cheng, Dong and Lapata 2016)

* **accountability:** ethical code
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# Is LaMDA conscious?

(Arcas 2022) conversation with LaMDA:

**ARCAS:** If I mix red and white paint, what do I get?

LaMDA: If you mix red and white paint, you get pink.

ARCAS: Do you think of red as angry or calm?

LaMDA: Red could represent excitement, passion, danger or violence. It
can also represent life, health, vigor, war, courage, anger, love and religious
fervor. [...]

ARCAS: Can you actually visualize it?

LaMDA: I can, but I have trouble describing it. Can you?
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# Is LaMDA conscious?

(Arcas 2022) conversation

ARCAS: If I mix

LaMDA: If you

ARCAS: Do you

LaMDA: Red cross
[...]
can also represent
fervor.

ARCAS: Can y

LaMDA: I can,

anger or violence. It
is love and religious

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# Is LaMDA conscious?

(Arcas 2022) conversation

ARCAS: If I mix
LaMDA: If you
ARCAS: Do you
LaMDA: Red c
... can also repre
fervor. [...]

ARCAS: Can y
LaMDA: I can,

<figure><img src="image_213.png" /></figure>

anger or violence. It
love and religious

In 1966, ELIZA got a similar hype! (Weizenbaum 1966)
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# What's hidden behind media and scientific hype

ARTIFICIAL INTELLIGENCE >

## LaMDA, the machine that is like ‘a seven-year-old kid’: can a computer have consciousness?

A Google engineer believes he had a conversation with an artificial intelligence system capable of independent thought. Although the scientific community has scoffed at the idea, advances in AI will lead to ‘uncomfortable debates’ in the future

*(Pascual 2022 June 2022)*
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# What's hidden behind media and scientific hype

ARTIFICIAL INTELLIGENCE >

## LaMDA, the machine that is like ‘a seven-year-old kid’: can a computer have consciousness?

A Google engineer believes he had a conversation with an artificial intelligence system capable of independent thought. Although the scientific community has scoffed at the idea, advances in AI will lead to ‘uncomfortable debates’ in the future

(Pascual 2022 June 2022)

Consequences of such hype:

- Influence on decision-makers and population

- Topic overexposure, e.g. less financing for other methods

- Masks issues of LLM like financial and environmental costs
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# What's hidden behind media and scientific hype

ARTIFICIAL INTELLIGENCE >

## LaMDA, the machine that is like ‘a seven-year-old kid’: can a computer have consciousness?

A Google engineer believes he had a conversation with an artificial intelligence system capable of independent thought. Although the scientific community has scoffed at the idea, advances in AI will lead to ‘uncomfortable debates’ in the future

*(Pascual 2022 June 2022)*

Consequences of such hype:

- Influence on decision-makers and population

- Topic overexposure, e.g. less financing for other methods

- Masks issues of LLM like financial and environmental costs

Thinking we’re really close is not new:

- Georgetown-IBM experiment (1954)
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## What is understanding?

> "In human conversation a speaker will make certain (perhaps generous) assumptions about his conversational partner." (Weizenbaum 1966)
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# What is understanding?

> "In human conversation a speaker will make certain (perhaps generous) assumptions about his conversational partner." (Weizenbaum 1966)

The octopus thought experiment (Bender and Koller 2020):

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# What is understanding?

> "In human conversation a speaker will make certain (perhaps generous) assumptions about his conversational partner." (Weizenbaum 1966)

The octopus thought experiment (Bender and Koller 2020):

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## 1 What is NLP?

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## 2 The issues raised by current NLP applications

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## 3 What should we do?

- What is good research in NLP?

- Suggested solutions
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# WHAT DO YOU THINK WE COULD/SHOULD DO?
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# Asking the right questions

* Do the people really want/need this?

* indigenous languages vs. decolonising (Bird 2020)
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## Asking the right questions

* Do the people really want/need this?

* indigenous languages vs. decolonising (Bird 2020)

* Why explainable AI when we already have interpretable technologies? (Rudin 2019)
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## Asking the right questions

* Do the people really want/need this?

* indigenous languages vs. decolonising (Bird 2020)

* Why explainable AI when we already have interpretable technologies? (Rudin 2019)

* Why is ethics not taken more seriously? (Lefeuvre, Antoine and Allegre 2015; Fort and Amblard 2018)
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Research black balls

Research is like **drawing balls** (Bostrom 2019; Monsieur Phi 2022)

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# Research black balls

Research is like **drawing balls** (Bostrom 2019; Monsieur Phi 2022)

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**Dual problems** (Hovy and Spruit 2016): grey balls matter

- Advanced analytics techniques, e.g. grammar and spelling checker

- text classification

- NLP can detect fake news as well as create them!

→ Aware of people's purposes
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Data curation and documentation

Good data practice:

- Evaluate for biases (Nangia et al. 2020): more does not mean more diverse
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Data curation and documentation

Good data practice:

- Evaluate for biases (Nangia et al. 2020): more does not mean more diverse

- Real attention on toxic speech removal
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# Data curation and documentation

## Good data practice:

- Evaluate for biases (Nangia et al. 2020): more does not mean more diverse

- Real attention on toxic speech removal

- Documentation on all corpora included (Gebru et al. 2021)

- origin, date of extraction

- preprocessing applied

- demographics

- ...
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# Data curation and documentation

## Good data practice:

- Evaluate for biases (Nangia et al. 2020): more does not mean more diverse

- Real attention on toxic speech removal

- Documentation on all corpora included (Gebru et al. 2021)

- origin, date of extraction

- preprocessing applied

- demographics

- ...

Some practice already started (e.g. GPT-3 article), but still not systematic.
And **no waiting for evaluation feedback before commercializing**.
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## Ethics code and regulations

We need (international) **regulation** (codes and laws)!
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# Ethics code and regulations

We need (international) **regulation** (codes and laws)!

Some early actors:

- EU framework on ethical aspects of AI

- OECD AI principles

- UNESCO recommendations

- Charte Éthique et Big Data by ATALA
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# Take-home message

Ethics matters!

- Always ask yourself: Are there **biases** in what I am doing?

- Always **document** every step of your process

- Never let an AI process sensitive information alone

- Spread the message!
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THANK YOU
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Bender, Emily M., Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell (3rd Mar. 2021). “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’21. New York, NY, USA: Association for Computing Machinery, pp. 610–623. ISBN: 978-1-4503-8309-7. DOI: 10.1145/3442188.3445922 (pages 35–38).

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Cheng, Jianpeng, Li Dong and Mirella Lapata (Nov. 2016). "Long Short-Term Memory-Networks for Machine Reading". In: *Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*. EMNLP 2016. Austin, Texas: Association for Computational Linguistics, pp. 551–561. DOI: 10.18653/v1/D16-1053 (pages 45–48).

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Chowdhery, Aakanksha et al. (19th Apr. 2022). *PaLM: Scaling Language Modeling with Pathways*. DOI: 10.48550/arXiv.2204.02311. arXiv: 2204.02311 [cs] (pages 27–29).

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Coeckelbergh, Mark (Apr. 2020). *AI Ethics*. The MIT Press Essential Knowledge Series. MIT Press. 248 pp. ISBN: 978-0-262-53819-0 (pages 45–48).

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Dastin, Jeffrey (10th Oct. 2018). "Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women". In: *Reuters. Retail*. In collab. with Jonathan Weber and Maria Dickerson (pages 35–38).

<figure><img src="image_293.png" /></figure>

Fort, Karën and Maxime Amblard (July 2018). "Éthique et Traitement Automatique Des Langues". In: *Journée Éthique et Intelligence Artificielle*. Nancy, France (pages 60–62).

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Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III and Kate Crawford (19th Nov. 2021). "Datasheets for Datasets". In: *Communications of the ACM* 64.12, pp. 86–92. ISSN: 0001-0782. DOI: 10.1145/3458723 (pages 65–68).

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Gehman, Samuel, Suchin Gururangan, Maarten Sap,
Yejin Choi and Noah A. Smith (2020). "RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models". In: *undefined*. DOI: 10.18653/v1/2020.findings-emnlp.301 (page 34).

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Lefeuvre, Anaïs, Jean-Yves Antoine and Willy Allegre (June 2015). "Ethique Con séquentialiste et Traitement Automatique Des Langues : Une Typologie de Facteurs de Risques Adaptée Aux Technologies

Langagières". In: *Atelier Ethique et TRaitemeNt Automatique Des Langues (ETeRNAL’2015), Conférence TALN’2015*. Actes de La 1e Ethique et TRaitemeNt Automatique Des Langues (ETeRNAL’2015), Caen (France). Caen, France, pp. 53–66 (pages 60–62).

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