10min
Read
Insights: The EU AI Act and its Impact on Innovation
by
Nuklai

The European Council has approved the Artificial Intelligence Act. This comprehensive law enforces the strict observance of EU copyright law. It also requires disclosure on model training and data transparency. 

The law’s creators say it will make tech innovations more "human-centric” by enhancing transparency, trust, and accountability in new technologies like AI.

While the AI Act will only apply within the EU, businesses beyond Europe will feel its impact. All businesses training or using AI outside the EU but using EU data in their LLMs will need to adhere to the law.

Failure to comply with the new regulations risks hefty fines. According to a CNBC report, companies that breach the laws could face fines up to  7% of their yearly turnover or €35 million, whichever is higher.

What is the  EU AI Act impact on the AI industry?

The EU AI Act adopts a risk-based approach. So, applications of AI technology that can cause more harm to society face more stringent requirements. Examples of high-risk applications of AI include the use of the technology within medical devices, autonomous vehicles, the education industry and financial services industry. 

Within these industries the impact of the technology and potential for bias within the data it is trained on is high. 

Tech companies running powerful Large Language Models (LLMs) models like GPT-4 or Google's Gemini will also face stricter requirements. 

Generative AI platforms will especially need to adhere to Article 10 of the new AI Act which emphasizes data transparency to "protect private life and the confidentiality of communications... by way of providing conditions for any storing of personal and non-personal data in, and access from, terminal equipment".

Under the AI Act both companies and AI labs that develop, use or train the AI models need to disclose for what purpose they will use users data and disclose to them their specific intent. Doing so helps give clarity and maintains accountability in the use of data. It keeps users aware of how, where, and why their data is being used.

Steps for Compliance

Companies must now create systems and data policies that demystify the data on which their models train. They must explain exactly what data a model uses to make its statements and conclusions. Ideally according to the EU this will enhance public trust, verifiability, and reliability on AI systems.

When you want to start making use of AI models in your business process, start documenting what data you would potentially require and write down the purpose for which you want to use the data. 

Identify your organization's data and governance processes surrounding this data and clarify the usage of data in user policies and other terms and conditions. In addition you may need to redesign existing data strategies to make sure you can explain how models come to their conclusions and on what data they draw their conclusions. 

The AI Act's mandatory data monitoring and transparency requirements will be a learning curve for most organizations. Fortunately, enterprises developing or using LLM models can leverage Nuklai’s Enterprise platform to unlock the full value of their data.

Nuklai's smart data ecosystem can provide transparency and traceability on data usage by large language models. We leverage verifiable records to provide an audit trail of which data was used by the large language model to come to its answers. 

Our audit trail feature can track data from its collection.  User consent can be obtained, while companies can even choose to compensate their users for their contributed data. 

The Nuklai platform also supports data anonymization. By rendering data unidentifiable, businesses that handle sensitive personal data will be also aligning with Article 10 of the Act. 

Reach out to our team of experts. We will work with you to understand your company's business needs and data opportunities.

‍

About Nuklai

Nuklai’s infrastructure helps enterprises simplify data access, usage, and understanding. Its smart data ecosystem unlocks data's true potential across organizations through processes that enhance data systems integration, innovation, and cooperation. 

Its streamlined, easy-to-use enterprise data platform, mapping, and enhancement processes turn businesses into truly data-first organizations. Nuklai’s enterprise data platform also links vendors and distributors, opening up data activation opportunities through AI model training, collaboration with leading businesses, or monetizing datasets. 

Follow Nuklai on X and LinkedIn to stay updated on the latest Nuklai news and updates.

Javascript
Python
PHP
const ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
const ApiKey = "[API_KEY]";
const DatasetId = "[DATASET_ID]";

const headers = {
  "Content-Type": "application/json",
  'authentication': ApiKey
}

ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries"
ApiKey = "[API_KEY]"
DatasetId = "[DATASET_ID]"

headers = {
  "Content-Type": "application/json",
  "authentication": ApiKey
}

$ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
$ApiKey = "[API_KEY]";
$DatasetId = "[DATASET_ID]";

$headers = [
  "Content-Type: application/json",
  "authentication: $ApiKey"
];
Javascript
Python
PHP
// @dataset represents your dataset rows as a table
const body = {
  sqlQuery: "select * from @dataset limit 5",
}

@dataset represents your dataset rows as a table
body = {
  "sqlQuery": "select * from @dataset limit 5"
}

// @dataset represents your dataset rows as a table
$body = [
  "sqlQuery" => "select * from @dataset limit 5"
];
Javascript
Python
PHP
cURL
const ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
const ApiKey = "[API_KEY]";
const DatasetId = "[DATASET_ID]";

const headers = {
  "Content-Type": "application/json",
  'authentication': ApiKey
}

// @dataset represents your dataset rows as a table
const body = {
  sqlQuery: "select * from @dataset limit 5",
}

// make request
fetch(ApiUrl.replace(':datasetId', DatasetId), {
  method: "POST",
  headers: headers,
  body: JSON.stringify(body), // convert to json object
})
  .then((response) => response.json())
  .then((data) => {
    console.log(data);
  })
  .catch((error) => {
    console.error(error);
  });

import requests
import json

ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries"
ApiKey = "[API_KEY]"
DatasetId = "[DATASET_ID]"

headers = {
  "Content-Type": "application/json",
  "authentication": ApiKey
}

# @dataset represents your dataset rows as a table
body = {
  "sqlQuery": "select * from @dataset limit 5"
}

# make request
url = ApiUrl.replace(':datasetId', DatasetId)
try:
  response = requests.post(url, headers=headers, data=json.dumps(body))
  data = response.json()
  print(data)
except requests.RequestException as error:
  print(f"Error: {error}")

$ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
$ApiKey = "[API_KEY]";
$DatasetId = "[DATASET_ID]";

$headers = [
  "Content-Type: application/json",
  "authentication: $ApiKey"
];

// @dataset represents your dataset rows as a table
$body = [
  "sqlQuery" => "select * from @dataset limit 5"
];

// make request
$ch = curl_init(str_replace(':datasetId', $DatasetId, $ApiUrl));

curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($body)); // convert to json object
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, TRUE);

$result = curl_exec($ch);
curl_close($ch);

echo $result;

curl -X POST 'https://api.nukl.ai/api/public/v1/datasets/[DATASET_ID]/queries' \
  -H 'Content-Type: application/json' \
  -H 'authentication: [API_KEY]' \
  -d '{"sqlQuery":"select * from @dataset limit 5"}'
Javascript
Python
PHP
cURL
const ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries/:jobId";
const ApiKey = "[API_KEY]";
const DatasetId = "[DATASET_ID]";
const JobId = "[JOB_ID]"; // retrieved from /queries request

const headers = {
  "Content-Type": "application/json",
  'authentication': ApiKey
}

// make request
fetch(ApiUrl.replace(':datasetId', DatasetId).replace(':jobId', JobId), {
  method: "GET",
  headers: headers
})
  .then((response) => response.json())
  .then((data) => {
    console.log(data);
  })
  .catch((error) => {
    console.error(error);
  });

import requests

ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries/:jobId"
ApiKey = "[API_KEY]"
DatasetId = "[DATASET_ID]"
JobId = "[JOB_ID]"  # retrieved from /queries request

headers = {
  "Content-Type": "application/json",
  "authentication": ApiKey
}

# make request
url = ApiUrl.replace(':datasetId', DatasetId).replace(':jobId', JobId)
try:
  response = requests.get(url, headers=headers)
  data = response.json()
  print(data)
except requests.RequestException as error:
  print(f"Error: {error}")

$ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries/:jobId";
$ApiKey = "[API_KEY]";
$DatasetId = "[DATASET_ID]";
$JobId = "[JOB_ID]"; // retrieved from /queries request

$headers = [
  "Content-Type: application/json",
  "authentication: $ApiKey"
];

// @dataset represents your dataset rows as a table
$body = [
  "sqlQuery" => "select * from @dataset limit 5"
];

// make request
$ch = curl_init(str_replace(array(':datasetId', ':jobId'), array($DatasetId, $JobId), $ApiUrl));

curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, TRUE);

$result = curl_exec($ch);
curl_close($ch);

echo $result;

curl 'https://api.nukl.ai/api/public/v1/datasets/[DATASET_ID]/queries/[JOB_ID]' \
  -H 'Content-Type: application/json' \
  -H 'authentication: [API_KEY]'